<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/2408.05395"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2603.10941"/>
	<rdf:li rdf:resource="https://academic.oup.com/bib/article/26/6/bbaf661/8376798?login=false"/>
	<rdf:li rdf:resource="https://www.mdpi.com/2504-3110/9/8/528"/>
	<rdf:li rdf:resource="https://biomath.math.bas.bg/biomath/index.php/biomath/article/view/j.biomath.2025.08.055"/>
	<rdf:li rdf:resource="https://www.mdpi.com/2504-3110/9/7/475"/>
	<rdf:li rdf:resource="https://www.nature.com/articles/s41467-025-59288-y"/>
	<rdf:li rdf:resource="https://www.cambridge.org/core/journals/philosophy-of-science/article/navigating-in-the-dark/C02547A095F27160DE525E3B3FDA25CE?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2501.06123"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2304.06670"/>
	<rdf:li rdf:resource="https://carcinisation.com/2023/08/22/against-automaticity/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2206.15099"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2405.17919"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2102.07844"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2203.06498"/>
	<rdf:li rdf:resource="https://going-medieval.com/2020/10/16/on-colonial-mindsets-and-the-myth-of-medieval-europe-in-isolation-from-the-muslim-world/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1906.05746"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1810.02241"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2102.08351"/>
	<rdf:li rdf:resource="https://www.biorxiv.org/content/10.1101/2021.02.16.431544v1"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1904.02063"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.06754"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2001.08049"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/cond-mat/0605387"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1909.13653"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1810.02909"/>
	<rdf:li rdf:resource="http://philsci-archive.pitt.edu/16202/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1601.00013"/>
	<rdf:li rdf:resource="http://contingentmagazine.org/2019/05/18/in-memoriam-panbabylonianism/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1512.00933"/>
	<rdf:li rdf:resource="http://blog.mrmeyer.com/2019/all-learning-is-modeling-my-five-minute-talk-at-cime2019-that-made-things-weird/"/>
	<rdf:li rdf:resource="https://pages.ucsd.edu/~mckenzie/Lopes1991Theory&amp;Psychology.pdf"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1510.02315"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/math/9404236"/>
	<rdf:li rdf:resource="https://theamericanscholar.org/a-pleasure-to-read-you"/>
	<rdf:li rdf:resource="https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1802.07029"/>
	<rdf:li rdf:resource="https://hackernoon.com/explainable-ai-wont-deliver-here-s-why-6738f54216be"/>
	<rdf:li rdf:resource="https://blogs.scientificamerican.com/observations/quantum-epistemology-for-business/"/>
	<rdf:li rdf:resource="http://rspb.royalsocietypublishing.org/content/282/1813/20151019"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1809.10756"/>
	<rdf:li rdf:resource="https://www.biorxiv.org/content/early/2018/09/19/421842"/>
	<rdf:li rdf:resource="http://evonomics.com/the-only-woman-to-win-the-nobel-prize-economics-debunked/"/>
	<rdf:li rdf:resource="https://johncarlosbaez.wordpress.com/2018/04/27/props-in-network-theory/"/>
	<rdf:li rdf:resource="http://psych-networks.com/meaning-model-equivalence-network-models-latent-variables-theoretical-space/"/>
	<rdf:li rdf:resource="https://lispcast.com/church-vs-curry-types/"/>
	<rdf:li rdf:resource="https://mobile.nytimes.com/2018/02/28/opinion/corporate-america-suppressing-wages.html"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1802.02627"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1703.10651"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1710.03453"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1703.04977"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1312.7604"/>
	<rdf:li rdf:resource="http://thearchdruidreport.blogspot.com/2017/02/perched-on-wheel-of-time.html"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1612.02483"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1508.05837"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1604.04647"/>
	<rdf:li rdf:resource="http://biorxiv.org/content/early/2016/12/23/096438?rss=1%2522"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1612.02540"/>
	<rdf:li rdf:resource="https://www.researchgate.net/publication/301698580_On_Computational_Explanations_Synthese_in_press_DOI_101007s11229-016-1101-5"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1608.05226"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1607.06274"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1606.03490#"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1604.01674"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1601.03243"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1505.01396"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1604.08412"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1603.00984"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1512.07450"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1510.05574"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1508.05990"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://arxiv.org/abs/2408.05395">
    <title>[2408.05395] The evolution of systems biology and systems medicine: From mechanistic models to uncertainty quantification</title>
    <dc:date>2026-05-24T10:49:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2408.05395</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial to driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating biological systems and revealing biochemical regulatory mechanisms. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks and uncover biochemical mechanisms in healthy and diseased states. The rapid development of high-throughput sequencing techniques and computational tools has recently enabled models that span multiple scales, often integrating signaling, gene regulatory, and metabolic networks. These multiscale models enable comprehensive investigations of cellular networks and thus reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from classical mechanistic models to larger, multiscale models that integrate multiple layers of cellular networks. We introduce several examples of models of hypertrophic cardiomyopathy, exercise, and cancer cell proliferation. Additionally, we discuss methods that increase the certainty and accuracy of model predictions. Integrating multiscale models has become a powerful tool for understanding disease and inspiring drug discoveries by incorporating omics data within the cell and across tissues and organisms.
]]></description>
<dc:subject>systems-biology molecular-machinery medicine medical-technology network-theory pharmaceutical machine-learning rather-interesting models-and-modes reaction-networks systems-thinking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d028f0a81a52/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medicine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pharmaceutical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-thinking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2603.10941">
    <title>[2603.10941] Covariate-adjusted statistical dependence representation through partial copulas: bounds and new insights</title>
    <dc:date>2026-05-22T11:17:27+00:00</dc:date>
    <link>https://arxiv.org/abs/2603.10941</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we revisit the notion of partial copula, originally introduced to test conditional independence, highlighting its capability to represent the dependence between two random variables after removing their dependence with a covariate. Building upon results previously presented in the literature, we show that partial copulas can be seen as a nonlinear analogue of partial correlation. Then, we prove several results showing how dependence properties of the conditional copulas constrain the form of the partial copula. Finally, a simulation study is conducted to illustrate the results and to show the potential of partial copula as a way to describe covariate-adjusted statistical dependence. This highlights the potential of the method to be used in causal inference problems and recover the true sign of a causal effect.
]]></description>
<dc:subject>statistics correlation models models-and-modes to-understand representation causality via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:36c7daa074e0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:correlation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/bib/article/26/6/bbaf661/8376798?login=false">
    <title>Beyond metaphor: quantitative reconstruction of Waddington landscape and exploration of cellular behavior | Briefings in Bioinformatics | Oxford Academic</title>
    <dc:date>2026-02-23T14:17:02+00:00</dc:date>
    <link>https://academic.oup.com/bib/article/26/6/bbaf661/8376798?login=false</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Originally proposed as a conceptual metaphor, the Waddington landscape was used to illustrate the directional nature of embryonic development and the relative stability of distinct developmental states. While the Waddington landscape offers a valuable conceptual framework for understanding cellular dynamics, its quantitative reconstruction remains a significant challenge in systems biology and biophysics. Recent methodological advances in single-cell omics technologies, computational modeling approaches, and nonlinear dynamical systems theory have facilitated progress toward quantitative reconstruction of the Waddington landscape, thereby transforming this heuristic metaphor into a predictive theoretical framework. In this review, we summarize the theoretical foundations of the Waddington landscape, categorize current computational and mathematical approaches for the Waddington landscape reconstruction. Meanwhile, we highlight the potential applications and inherent limitations of these approaches in characterizing cellular behaviors, predicting cell fate decisions, and modulating developmental trajectories.]]></description>
<dc:subject>theoretical-biology real-biology models-and-modes somebody-else-will-eventually-show-them-at-the-Academy! fitness-landscapes Waddington-landscapes systems-biology evo-devo</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b570d588d518/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:real-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:somebody-else-will-eventually-show-them-at-the-Academy!"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Waddington-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evo-devo"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mdpi.com/2504-3110/9/8/528">
    <title>Design and Control of Fractional-Order Systems Based on Fractal Operators</title>
    <dc:date>2025-08-18T14:41:29+00:00</dc:date>
    <link>https://www.mdpi.com/2504-3110/9/8/528</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In recent years, we have abstracted physical fractal space from biological structures and movements within living organisms, revealing the profound intrinsic connections between fractional order time and fractional-dimensional space, and providing partial explanations for the sources and orders of fractional order. We have confirmed that the topological invariants of fractal cells, the order of physical components, and the mismatch of spatiotemporal order are important factors determining the fractional order of operators. This paper is a continuation of the previous work. Inspired by bone fractal operators, this article attempts to identify other factors that affect the order of operators. Specifically, the following contents are included: (1) originating from the bone fractal operators, we present the construction process of the “apparent half-order” system; (2) using the Schiessel–Blumen model as the comparative object, we analyze the origin and characteristics of the “γ-order” system; (3) using the continued fraction theory and operatorization thought as the link, we establish the design and control method for general fractional-order systems, and discuss the factors affecting the order of fractional-order operators.
]]></description>
<dc:subject>fractals continued-fractions dynamical-systems nonlinear-dynamics rather-interesting to-write-about to-simulate consider:recursion consider:representation diffy-Qs models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:21eb00f4dd1f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fractals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:continued-fractions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:recursion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diffy-Qs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://biomath.math.bas.bg/biomath/index.php/biomath/article/view/j.biomath.2025.08.055">
    <title>A biological growth model using continued fraction of straight lines. Methodological aspects | BIOMATH</title>
    <dc:date>2025-08-12T13:12:27+00:00</dc:date>
    <link>https://biomath.math.bas.bg/biomath/index.php/biomath/article/view/j.biomath.2025.08.055</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[S-shaped curves are ubiquitous in biology especially when it comes to growth of a population or even an individual. Growth models such as the classical Verhulst-Pearl logistic growth equation and its extensions effectively model such S-shaped growth curves. Most of these models are parametrized by three or more parameters. In this work, continued fraction of straight lines has been applied to model S-shaped curves of biological growth through the use of only two parameters a and m. Here, m is the maximum growth rate and a is the parameter restricting the growth rate. The parameters a and m help to better interpret the data when compared to the logistic growth model since m represents factors promoting growth while a represents restricting factors of growth. This model is effective for modeling both population as well as individual growth, especially around the phase of rapid growth.
]]></description>
<dc:subject>models-and-modes curve-fitting rather-interesting continued-fractions numerical-methods to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83f520489747/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:curve-fitting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:continued-fractions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mdpi.com/2504-3110/9/7/475">
    <title>The Continued Fraction Structure in Physical Fractal Theory</title>
    <dc:date>2025-08-10T11:59:39+00:00</dc:date>
    <link>https://www.mdpi.com/2504-3110/9/7/475</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The objective of this study is to reveal the intrinsic connection between fractal operators in physical fractal spaces and continued fractions. The specific contributions include: (1) reviewing fundamental concepts of continued fractions and physical fractal theory; (2) establishing algebraic structure consistency between continued fractions and fractal operators through the medium of generation mappings; (3) discussing the convergence of fractal operators by employing theory from continued fraction analysis; and (4) confirming the correspondence between fixed points of infinite continued fractions and algebraic equations governing fractal operators.
]]></description>
<dc:subject>biophysics fractals continued-fractions models-and-modes rather-interesting simulation to-simulate to-write-about consider:nonlinearity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:faf23619376e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fractals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:continued-fractions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:nonlinearity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41467-025-59288-y">
    <title>Interactive symbolic regression with co-design mechanism through offline reinforcement learning | Nature Communications</title>
    <dc:date>2025-05-01T12:44:31+00:00</dc:date>
    <link>https://www.nature.com/articles/s41467-025-59288-y</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Symbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods and pre-trained transformer models, which mostly do not consider the integration of domain experts’ prior knowledge. To address these challenges, we propose the Symbolic Q-network, an advanced interactive framework for large-scale symbolic regression. Unlike previous transformer-based SR approaches, Symbolic Q-network leverages reinforcement learning without relying on a transformer-based decoder. Furthermore, we propose a co-design mechanism, where the Symbolic Q-network facilitates effective interaction with domain experts at any stage of the equation discovery process. Our extensive experiments demonstrate Sym-Q performs comparably to existing pretrained models across multiple benchmarks. Furthermore, our experiments on real-world cases demonstrate that the interactive co-design mechanism significantly enhances Symbolic Q-network’s performance, achieving greater performance gains than standard autoregressive models.

]]></description>
<dc:subject>symbolic-regression models-and-modes machine-learning rather-interesting reinvented-wheels to-understand metaheuristics reinforcement-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c682cbd2e528/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinvented-wheels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinforcement-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/navigating-in-the-dark/C02547A095F27160DE525E3B3FDA25CE?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues">
    <title>Navigating in the Dark | Philosophy of Science | Cambridge Core</title>
    <dc:date>2025-03-31T13:51:03+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/navigating-in-the-dark/C02547A095F27160DE525E3B3FDA25CE?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Issues</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This article introduces the snap hook methodology, a method used notably in astrochemistry as a way to indirectly validate and assess the accuracy of computational calculations in the absence of experimental or observational data. We argue that this methodology has tremendous potential for all computationally intensive scientific fields as a substitute for traditional verification and validation standards when those are not accessible and estimating the reliability of numerical predictions becomes a real difficulty. The goal of this article is to give to this method, which seems to be implicitly relied upon in many areas, a proper formulation, in order for philosophers of science to enter the debate and to highlight its undeniable potential in terms of interdisciplinary facilitation and knowledge transmission.

]]></description>
<dc:subject>to-understand via-? models-and-modes modeling science-from-a-distance looking-to-see looking-to-guess-better rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:58af6d1d35e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via-?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science-from-a-distance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-guess-better"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2501.06123">
    <title>[2501.06123] Numerical methods for Chaotic ODE</title>
    <dc:date>2025-01-16T19:30:47+00:00</dc:date>
    <link>https://arxiv.org/abs/2501.06123</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper explores backward error analysis for numerical solutions of ordinary differential equations, particularly focusing on chaotic systems. Three approaches are examined: residual assessment, the method of modified equations, and shadowing. We investigate how these methods explain the success of numerical simulations in capturing the behavior of chaotic systems, even when facing issues like spurious chaos introduced by numerical methods or suppression of chaos by numerical methods. Finally, we point out an open problem, namely to explain why the statistics of long orbits are usually correct, even though we do not have a theoretical guarantee why this should be so.
]]></description>
<dc:subject>nonlinear-dynamics models-and-modes rather-interesting to-write-about to-generalize consider:as-a-measure-of-robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:55f74e967669/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-generalize"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:as-a-measure-of-robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2304.06670">
    <title>[2304.06670] Do deep neural networks have an inbuilt Occam's razor?</title>
    <dc:date>2024-10-08T20:05:33+00:00</dc:date>
    <link>https://arxiv.org/abs/2304.06670</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components, we apply a Bayesian picture, based on the functions expressed by a DNN, to supervised learning. The prior over functions is determined by the network, and is varied by exploiting a transition between ordered and chaotic regimes. For Boolean function classification, we approximate the likelihood using the error spectrum of functions on data. When combined with the prior, this accurately predicts the posterior, measured for DNNs trained with stochastic gradient descent. This analysis reveals that structured data, combined with an intrinsic Occam's razor-like inductive bias towards (Kolmogorov) simple functions that is strong enough to counteract the exponential growth of the number of functions with complexity, is a key to the success of DNNs.
]]></description>
<dc:subject>simplicity-bias deep-learning neural-networks sufficiently-ornate-useful-things-will-generate-their-own-Occams models-and-modes to-understand consider:functional-approximation consider:populations ayyyy:waves-it-at-Cosma</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d73e92d09a35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simplicity-bias"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sufficiently-ornate-useful-things-will-generate-their-own-Occams"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:functional-approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:populations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ayyyy:waves-it-at-Cosma"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://carcinisation.com/2023/08/22/against-automaticity/">
    <title>Against Automaticity – Carcinisation</title>
    <dc:date>2024-07-08T11:40:12+00:00</dc:date>
    <link>https://carcinisation.com/2023/08/22/against-automaticity/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The automaticity theories named here are holdovers from the fading myth of the clockwork universe, of clockwork people. The myth began to be named by the end of the 19th century (by William James at least, who also named the related Religion of Healthy-MIndedness that is still with us today). I hope by naming a more specific incarnation of the myth here that I can promote its thematization so that it can continue to fade. A science of ourselves cannot be established by dressing woo in lab coats, clipboards, and the mathematical ideas of an antique physics. 

I invite anyone to be the Lakatos to my Feyerabend, and present Here’s Why Automaticity Is Real Actually, as mine is an extreme case and does not pretend to be a measured, balanced examination of the subject. I would not recommend that anyone superstitious attempt this project, however, for obvious reasons: if priming were true, such an effort could prove lethal.

]]></description>
<dc:subject>psychology cognition models-and-modes history-of-pop-science rather-interesting economics homo-whateverus rather-good</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:28a930d79932/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history-of-pop-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:homo-whateverus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-good"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.15099">
    <title>[2206.15099] Automatic generation of interpretable hyperelastic material models by symbolic regression</title>
    <dc:date>2024-07-03T11:01:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.15099</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we present a new procedure to automatically generate interpretable hyperelastic material models. This approach is based on symbolic regression which represents an evolutionary algorithm searching for a mathematical model in the form of an algebraic expression. This results in a relatively simple model with good agreement to experimental data. By expressing the strain energy function in terms of its invariants or other parameters, it is possible to interpret the resulting algebraic formulation in a physical context. In addition, a direct implementation of the obtained algebraic equation is possible. For the validation of the proposed approach, benchmark tests on the basis of the generalized Mooney-Rivlin model are presented. In all these tests, the chosen ansatz can find the predefined models. Additionally, this method is applied for the multi-axial loading data set of vulcanized rubber. Finally, a data set for a temperature-dependent thermoplastic polyester elastomer is evaluated. In latter cases, good agreement with the experimental data is obtained.
]]></description>
<dc:subject>symbolic-regression materials-science numerical-methods models models-and-modes to-write-about explainable-modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4c415c4559a1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explainable-modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2405.17919">
    <title>[2405.17919] Fisher's Legacy of Directional Statistics, and Beyond to Statistics on Manifolds</title>
    <dc:date>2024-07-01T14:19:11+00:00</dc:date>
    <link>https://arxiv.org/abs/2405.17919</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It will not be an exaggeration to say that R A Fisher is the Albert Einstein of Statistics. He pioneered almost all the main branches of statistics, but it is not as well known that he opened the area of Directional Statistics with his 1953 paper introducing a distribution on the sphere which is now known as the Fisher distribution. He stressed that for spherical data one should take into account that the data is on a manifold. We will describe this Fisher distribution and reanalyse his geological data. We also comment on the two goals he set himself in that paper, and how he reinvented the von Mises distribution on the circle. Since then, many extensions of this distribution have appeared bearing Fisher's name such as the von Mises Fisher distribution and the matrix Fisher distribution. In fact, the subject of Directional Statistics has grown tremendously in the last two decades with new applications emerging in Life Sciences, Image Analysis, Machine Learning and so on. We give a recent new method of constructing the Fisher type distribution which has been motivated by some problems in Machine Learning. The subject related to his distribution has evolved since then more broadly as Statistics on Manifolds which also includes the new field of Shape Analysis. We end with a historical note pointing out some correspondence between D'Arcy Thompson and R A Fisher related to Shape Analysis.
]]></description>
<dc:subject>statistics history-of-science directional-statistics probability-theory models-and-modes biography</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:76b988d8705b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:directional-statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biography"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.07844">
    <title>[2102.07844] &quot;From What I see, this makes sense&quot;: Seeing meaning in algorithmic results</title>
    <dc:date>2022-04-02T13:09:26+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.07844</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this workshop paper, we use an empirical example from our ongoing fieldwork, to showcase the complexity and situatedness of the process of making sense of algorithmic results; i.e. how to evaluate, validate, and contextualize algorithmic outputs. So far, in our research work, we have focused on such sense-making processes in data analytic learning environments such as classrooms and training workshops. Multiple moments in our fieldwork suggest that meaning, in data analytics, is constructed through an iterative and reflexive dialogue between data, code, assumptions, prior knowledge, and algorithmic results. A data analytic result is nothing short of a sociotechnical accomplishment - one in which it is extremely difficult, if not at times impossible, to clearly distinguish between 'human' and 'technical' forms of data analytic work. We conclude this paper with a set of questions that we would like to explore further in this workshop.
]]></description>
<dc:subject>rather-interesting science-studies tools models-and-modes sociology-of-engineering cultural-norms pattern-discovery to-write-about consider:genetic-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cda4e529074b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science-studies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sociology-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-norms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pattern-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2203.06498">
    <title>[2203.06498] The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning</title>
    <dc:date>2022-04-02T12:26:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2203.06498</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recent concerns that machine learning (ML) may be facing a reproducibility and replication crisis suggest that some published claims in ML research cannot be taken at face value. These concerns inspire analogies to the replication crisis affecting the social and medical sciences, as well as calls for greater integration of statistical approaches to causal inference and predictive modeling. A deeper understanding of what reproducibility concerns in research in supervised ML have in common with the replication crisis in experimental science can put the new concerns in perspective, and help researchers avoid "the worst of both worlds" that can emerge when ML researchers begin borrowing methodologies from explanatory modeling without understanding their limitations, and vice versa. We contribute a comparative analysis of concerns about inductive learning that arise in different stages of the modeling pipeline in causal attribution as exemplified in psychology versus predictive modeling as exemplified by ML. We identify themes that re-occur in reform discussions like overreliance on asymptotic theory and non-credible beliefs about real-world data generating processes. We argue that in both fields, claims from learning are implied to generalize outside the specific environment studied (e.g., the input dataset or subject sample, modeling implementation, etc.) but are often impossible to refute due to forms of underspecification. In particular, many errors being acknowledged in ML expose cracks in long-held beliefs that optimizing predictive accuracy using huge datasets absolves one from having to make assumptions about the underlying data generating process. We conclude by discussing rhetorical risks like error misdiagnosis that arise in times of methodological uncertainty.
]]></description>
<dc:subject>via:cshalizi statistics machine-learning reproducibility science-studies models-and-modes assumptions rather-interesting consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6d5f50bf1656/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reproducibility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science-studies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:assumptions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://going-medieval.com/2020/10/16/on-colonial-mindsets-and-the-myth-of-medieval-europe-in-isolation-from-the-muslim-world/">
    <title>On colonial mindsets and the myth of medieval Europe in isolation from the Muslim world – Going Medieval</title>
    <dc:date>2022-03-06T12:11:43+00:00</dc:date>
    <link>https://going-medieval.com/2020/10/16/on-colonial-mindsets-and-the-myth-of-medieval-europe-in-isolation-from-the-muslim-world/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Pro-imperialist historiography is the air that we breath here in the decaying carcasses of the modern Imperium. I am extremely sympathetic to the urge to celebrate non-white cultures, and I spend quite a lot of time doing so myself. However, to argue that this was happening without any contact with Europe, and that Europeans cannot think or enjoy luxuries without also being involved in a violent imperial enterprise is extremely dangerous. I know that the people who make this argument think they are being enlightened, but they are still making a pro-imperial argument when they trot out tired myths about the medieval period. We don’t undo the colonial historiography by agreeing with it. We need to write our own history which admits that every world culture has something useful and beautiful to offer us all, and that a better world can be achieved without the subjugation of others.

]]></description>
<dc:subject>history colonialism medieval-culture world-history historiography models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c2b75736cf00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:colonialism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medieval-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:world-history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:historiography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.05746">
    <title>[1906.05746] Nonlinear System Identification via Tensor Completion</title>
    <dc:date>2022-03-04T11:21:16+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.05746</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Function approximation from input and output data pairs constitutes a fundamental problem in supervised learning. Deep neural networks are currently the most popular method for learning to mimic the input-output relationship of a general nonlinear system, as they have proven to be very effective in approximating complex highly nonlinear functions. In this work, we show that identifying a general nonlinear function y=f(x1,…,xN) from input-output examples can be formulated as a tensor completion problem and under certain conditions provably correct nonlinear system identification is possible. Specifically, we model the interactions between the N input variables and the scalar output of a system by a single N-way tensor, and setup a weighted low-rank tensor completion problem with smoothness regularization which we tackle using a block coordinate descent algorithm. We extend our method to the multi-output setting and the case of partially observed data, which cannot be readily handled by neural networks. Finally, we demonstrate the effectiveness of the approach using several regression tasks including some standard benchmarks and a challenging student grade prediction task.
]]></description>
<dc:subject>approximation models-and-modes representation optimization system-identification tensors to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4cc8017689df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tensors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.02241">
    <title>[1810.02241] Recursion schemes, discrete differential equations and characterization of polynomial time computation</title>
    <dc:date>2022-01-26T13:44:39+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.02241</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This papers studies the expressive and computational power of discrete Ordinary Differential Equations (ODEs). It presents a new framework using discrete ODEs as a central tool for computation and provides several implicit characterizations of complexity and computability classes. 
The proposed framework presents an original point of view on complexity and computability classes. It also unifies in an elegant settings various constructions that have been proposed for characterizing these classes. This includes Cobham's and, Bellantoni and Cook's definition of polynomial time and later extensions on the approach, as well as recent characterizations of computability and complexity by classes of ordinary differential equations. It also helps understanding the relationships between analog computations and classical discrete models of computation theory. 
At a more technical point of view, this paper points out the fundamental role of linear (discrete) ordinary differential equations and classical ODE tools such as changes of variables to capture computability and complexity measures, or as a tool for programming various algorithms.
]]></description>
<dc:subject>diffy-Qs discrete-mathematics nonlinear-dynamics representation rather-interesting computational-complexity models-and-modes to-understand to-write-about consider:genetic-programming consider:approximation ReQ</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b5692977ee6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diffy-Qs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:discrete-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ReQ"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.08351">
    <title>[2102.08351] Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics</title>
    <dc:date>2022-01-01T14:06:43+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.08351</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a solution function, which is determined by assigning operators and operands to the nodes. The symbolic regression problem can be formulated as a nonconvex mixed-integer nonlinear program (MINLP), where binary variables are used to assign operators and nonlinear expressions are used to propagate data values through nonlinear operators such as square, square root, and exponential. We extend this formulation by adding new cuts that improve the solution of this challenging MINLP. We also propose a heuristic that iteratively builds an expression tree by solving a restricted MINLP. We perform computational experiments and compare our approach with a mixed-integer program-based method and a neural-network-based method from the literature.
]]></description>
<dc:subject>mathematical-programming symbolic-regression models-and-modes rather-interesting wheels-reinvented</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:54ba381c5ed0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wheels-reinvented"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/2021.02.16.431544v1">
    <title>A test statistic to quantify treelikeness in phylogenetics | bioRxiv</title>
    <dc:date>2021-03-12T14:35:15+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/2021.02.16.431544v1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Most phylogenetic analyses assume that the evolutionary history of an alignment (either that of a single locus, or of multiple concatenated loci) can be described by a single bifurcating tree, the so-called the treelikeness assumption. Treelikeness can be violated by biological events such as recombination, introgression, or incomplete lineage sorting, and by systematic errors in phylogenetic analyses. The incorrect assumption of treelikeness may then mislead phylogenetic inferences. To quantify and test for treelikeness in alignments, we develop a test statistic which we call the tree proportion. This statistic quantifies the proportion of the edge weights in a phylogenetic network that are represented in a bifurcating phylogenetic tree of the same alignment. We extend this statistic to a statistical test of treelikeness using a parametric bootstrap. We use extensive simulations to compare tree proportion to a range of related approaches. We show that tree proportion successfully identifies non-treelikeness in a wide range of simulation scenarios, and discuss its strengths and weaknesses compared to other approaches. The power of the tree-proportion test to reject non-treelike alignments can be lower than some other approaches, but these approaches tend to be limited in their scope and/or the ease with which they can be interpreted. Our recommendation is to test treelikeness of sequence alignments with both tree proportion and mosaic methods such as 3Seq. The scripts necessary to replicate this study are available at https://github.com/caitlinch/treelikeness

]]></description>
<dc:subject>statistics rather-interesting phylogenetics models-and-modes fittedness-of-hypotheses to-write-about consider:genetic-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:36a478f66aaf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:phylogenetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fittedness-of-hypotheses"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.02063">
    <title>[1904.02063] Generalized Variational Inference: Three arguments for deriving new Posteriors</title>
    <dc:date>2020-05-22T21:16:22+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.02063</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We advocate an optimization-centric view on and introduce a novel generalization of Bayesian inference. Our inspiration is the representation of Bayes' rule as infinite-dimensional optimization problem (Csiszar, 1975; Donsker and Varadhan; 1975, Zellner; 1988). First, we use it to prove an optimality result of standard Variational Inference (VI): Under the proposed view, the standard Evidence Lower Bound (ELBO) maximizing VI posterior is preferable to alternative approximations of the Bayesian posterior. Next, we argue for generalizing standard Bayesian inference. The need for this arises in situations of severe misalignment between reality and three assumptions underlying standard Bayesian inference: (1) Well-specified priors, (2) well-specified likelihoods, (3) the availability of infinite computing power. Our generalization addresses these shortcomings with three arguments and is called the Rule of Three (RoT). We derive it axiomatically and recover existing posteriors as special cases, including the Bayesian posterior and its approximation by standard VI. In contrast, approximations based on alternative ELBO-like objectives violate the axioms. Finally, we study a special case of the RoT that we call Generalized Variational Inference (GVI). GVI posteriors are a large and tractable family of belief distributions specified by three arguments: A loss, a divergence and a variational family. GVI posteriors have appealing properties, including consistency and an interpretation as approximate ELBO. The last part of the paper explores some attractive applications of GVI in popular machine learning models, including robustness and more appropriate marginals. After deriving black box inference schemes for GVI posteriors, their predictive performance is investigated on Bayesian Neural Networks and Deep Gaussian Processes, where GVI can comprehensively improve upon existing methods.
]]></description>
<dc:subject>models-and-modes to-understand probability-theory machine-learning define-your-terms performance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ae6c80a8d812/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06754">
    <title>[1908.06754] A New Deterministic Technique for Symbolic Regression</title>
    <dc:date>2020-05-17T22:24:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06754</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is deterministic, and does not involve the creation of a population of initial solutions. Instead of it, a simple expression is being grown until it fits the data. The experiments performed show that the results are as good as other Machine Learning methods, in a very low computational time. Another advantage of this technique is that the complexity of the expressions can be limited, so the system can return mathematical expressions that can be easily analysed by the user, in opposition to other techniques like GSGP.
]]></description>
<dc:subject>symbolic-regression algorithms models-and-modes FFX to-write-about to-simulate consider:robustness consider:NFL</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:590af861b5d1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:FFX"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:NFL"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2001.08049">
    <title>[2001.08049] On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation</title>
    <dc:date>2020-05-13T23:45:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2001.08049</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Uncertainty quantification for deep learning is a challenging open problem. Bayesian statistics offer a mathematically grounded framework to reason about uncertainties; however, approximate posteriors for modern neural networks still require prohibitive computational costs. We propose a family of algorithms which split the classification task into two stages: representation learning and uncertainty estimation. We compare four specific instances, where uncertainty estimation is performed via either an ensemble of Stochastic Gradient Descent or Stochastic Gradient Langevin Dynamics snapshots, an ensemble of bootstrapped logistic regressions, or via a number of Monte Carlo Dropout passes. We evaluate their performance in terms of \emph{selective} classification (risk-coverage), and their ability to detect out-of-distribution samples. Our experiments suggest there is limited value in adding multiple uncertainty layers to deep classifiers, and we observe that these simple methods strongly outperform a vanilla point-estimate SGD in some complex benchmarks like ImageNet.
]]></description>
<dc:subject>machine-learning representation uncertainty models-and-modes classification to-generalize to-write-about to-simulate consider:genetic-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:58a7dcf1fea6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-generalize"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/cond-mat/0605387">
    <title>[cond-mat/0605387] The sloppy model universality class and the Vandermonde matrix</title>
    <dc:date>2020-01-12T14:23:55+00:00</dc:date>
    <link>https://arxiv.org/abs/cond-mat/0605387</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy; the system behavior depends only on a few `stiff' combinations of the parameters and is unchanged as other `sloppy' parameter combinations vary by orders of magnitude. We contrast examples of sloppy models (from systems biology, variational quantum Monte Carlo, and common data fitting) with systems which are not sloppy (multidimensional linear regression, random matrix ensembles). We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form, with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.
]]></description>
<dc:subject>models-and-modes random-systems nonlinear-dynamics rather-interesting theoretical-physics feature-selection sampling to-understand to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f691df62b4d9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:random-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.13653">
    <title>[1909.13653] Model Pluralism</title>
    <dc:date>2019-10-11T12:55:53+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.13653</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling-practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: (i) any successful analysis of models must target sets of models, their multiplicity of functions within science, and their scientific context and history and (ii) for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z.
]]></description>
<dc:subject>philosophy-of-science models-and-modes rather-interesting to-write-about collective-intelligence pragmatism (maybe-by-the-back-door) define-your-terms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2aedd390c0ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:(maybe-by-the-back-door)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.02909">
    <title>[1810.02909] On the Art and Science of Machine Learning Explanations</title>
    <dc:date>2019-08-05T14:23:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.02909</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This text discusses several popular explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the explanatory methods are accepted tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree surrogate models, individual conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), partial dependence plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable application domain. Along with descriptions of these methods, this text presents real-world usage recommendations supported by a use case and public, in-depth software examples for reproducibility.
]]></description>
<dc:subject>via:cshalizi boy-I-wish-NN-hadn't-stolen-the-field to-read to-write-about consider:genetic-programming consider:analogical-models system-of-professions to-extend visualization explanation models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:af1d2a610e30/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:boy-I-wish-NN-hadn't-stolen-the-field"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:analogical-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-extend"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/16202/">
    <title>The theory of games as a tool for the social epistemologist - Philsci-Archive</title>
    <dc:date>2019-07-29T10:42:28+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/16202/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Traditionally, epistemologists have distinguished between epistemic and pragmatic goals. In so doing, they presume that much of game theory is irrelevant to epistemic enterprises. I will show that this is a mistake. Even if we restrict attention to purely epistemic motivations, members of epistemic groups will face a multitude of strategic choices. I illustrate several contexts where individuals who are concerned solely with the discovery of truth will nonetheless face difficult game theoretic problems. Examples of purely epistemic coordination problems and social dilemmas will be presented. These show that there is a far deeper connection between economics and epistemology than previous appreciated.

]]></description>
<dc:subject>economics philosophy political-economy models-and-modes pragmatism utility rather-interesting science-studies to-read game-theory social-psychology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b30777c21f35/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:political-economy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:utility"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:science-studies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-psychology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1601.00013">
    <title>[1601.00013] A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function</title>
    <dc:date>2019-07-25T10:54:46+00:00</dc:date>
    <link>https://arxiv.org/abs/1601.00013</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this paper, we consider constructive approximation on any finite interval of ℝ by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function σ providing approximation to an arbitrary continuous function within any degree of accuracy. This algorithm is implemented in a computer program, which computes the value of σ at any reasonable point of the real axis.]]></description>
<dc:subject>neural-networks approximation models-and-modes old-results-in-new-clothes to-simulate to-write-about perceptrons</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5372b755cb02/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:old-results-in-new-clothes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:perceptrons"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://contingentmagazine.org/2019/05/18/in-memoriam-panbabylonianism/">
    <title>In Memoriam: Panbabylonianism - CONTINGENT</title>
    <dc:date>2019-06-13T11:15:47+00:00</dc:date>
    <link>http://contingentmagazine.org/2019/05/18/in-memoriam-panbabylonianism/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[You may not have heard of Panbabylonianism, but it was quite powerful at the turn of the 20th century, thanks especially to the work of German linguists and archaeologists like Friedrich Delitzsch and Hugo Winckler. As the name suggests, this school of thought asserted that everything had its cultural origins in Mesopotamia, the Tigris-Euphrates river system that gave birth to the ancient Sumerian, Babylonian, and Assyrian civilizations.1

]]></description>
<dc:subject>historiography models-and-modes scholarship lost-threads rather-interesting colonialism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:947b036aa4b9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:historiography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:scholarship"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lost-threads"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:colonialism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1512.00933">
    <title>[1512.00933] Probabilistic Integration: A Role in Statistical Computation?</title>
    <dc:date>2019-04-16T09:56:15+00:00</dc:date>
    <link>https://arxiv.org/abs/1512.00933</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical methods that enable the coherent propagation of probabilities through a (possibly deterministic) computational work-flow. This paper examines the case for probabilistic numerical methods in routine statistical computation. Our focus is on numerical integration, where a probabilistic integrator is equipped with a full distribution over its output that reflects the presence of an unknown numerical error. Our main technical contribution is to establish, for the first time, rates of posterior contraction for these methods. These show that probabilistic integrators can in principle enjoy the "best of both worlds", leveraging the sampling efficiency of Monte Carlo methods whilst providing a principled route to assess the impact of numerical error on scientific conclusions. Several substantial applications are provided for illustration and critical evaluation, including examples from statistical modelling, computer graphics and a computer model for an oil reservoir.
]]></description>
<dc:subject>machine-learning probability-theory models-and-modes representation to-understand simulation sampling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3885347f0d8e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.mrmeyer.com/2019/all-learning-is-modeling-my-five-minute-talk-at-cime2019-that-made-things-weird/">
    <title>All Learning Is Modeling: My Five-Minute Talk at #CIME2019 That Made Things Weird – dy/dan</title>
    <dc:date>2019-03-21T11:39:32+00:00</dc:date>
    <link>http://blog.mrmeyer.com/2019/all-learning-is-modeling-my-five-minute-talk-at-cime2019-that-made-things-weird/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[I contributed to a panel on mathematical modeling panel at MSRI this week – five minutes of prepared remarks and then answers to a couple of questions.

Sol Garfunkel, a co-panelist and personal hero, would later call my introductory remarks “completely wrong.” A university professor called them “dangerous.”

I mention those reviews not to marshal sympathy. I’m really happy with my remarks and I don’t think I was misunderstood! I’m mentioning them to acknowledge that my remarks caused a lot of anxiety among people who call themselves mathematical modelers. I’ll respond to some of those anxieties below.

]]></description>
<dc:subject>models-and-modes modeling essay yes to-write-about empathy define-your-terms system-of-professions formalization deformalization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0b3340ca8698/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:essay"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:yes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:empathy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deformalization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pages.ucsd.edu/~mckenzie/Lopes1991Theory&amp;Psychology.pdf">
    <title>[PDF] The Rhetoric of Irrationality</title>
    <dc:date>2019-03-03T12:17:02+00:00</dc:date>
    <link>https://pages.ucsd.edu/~mckenzie/Lopes1991Theory&amp;Psychology.pdf</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The popularity of the `biases and heuristics' literature is examined critically in terms of the rhetorical factors that have mediated widely published claims that human judgment abilities are poor and even irrational. The logic of the original experiments is examined as well as the factors that cause that logic to be ambiguous and the implications of the experiments to be misrepresented. Questionable use of evaluative language in scientific articles and secondary gains to outside authors who spread the bias message are also examined.]]></description>
<dc:subject>cognition experiment planning heuristics models-and-modes via:? rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6c9d002e0554/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1510.02315">
    <title>[1510.02315] Mean-field limit for collective behavior models with sharp sensitivity regions</title>
    <dc:date>2019-02-28T11:14:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1510.02315</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We rigorously show the mean-field limit for a large class of swarming individual based models with local sharp sensitivity regions. For instance, these models include nonlocal repulsive-attractive forces locally averaged over sharp vision cones and Cucker-Smale interactions with discontinuous communication weights. We construct global-in-time defined notion of solutions through a differential inclusion system corresponding to the particle descriptions. We estimate the error between the solutions to the differential inclusion system and weak solutions to the expected limiting kinetic equation by employing tools from optimal transport theory. Quantitative bounds on the expansion of the 1-Wasserstein distance along flows based on a weak-strong stability estimate are obtained. We also provide different examples of realistic sensitivity sets satisfying the assumptions of our main results.
]]></description>
<dc:subject>nonlinear-dynamics not-the-represenation-I-expected swarms models-and-modes to-understand maybe-translate huh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4187624b08e3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:not-the-represenation-I-expected"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:swarms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:maybe-translate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:huh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/math/9404236">
    <title>[math/9404236] On proof and progress in mathematics</title>
    <dc:date>2019-02-07T11:36:51+00:00</dc:date>
    <link>https://arxiv.org/abs/math/9404236</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In response to Jaffe and Quinn [math.HO/9307227], the author discusses forms of progress in mathematics that are not captured by formal proofs of theorems, especially in his own work in the theory of foliations and geometrization of 3-manifolds and dynamical systems.
]]></description>
<dc:subject>philosophy-of-science mathematics models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:235a6a6a241c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://theamericanscholar.org/a-pleasure-to-read-you">
    <title>The American Scholar: A Pleasure to Read You - Arthur Krystal</title>
    <dc:date>2019-01-05T13:22:48+00:00</dc:date>
    <link>https://theamericanscholar.org/a-pleasure-to-read-you</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Something, I fear, goes missing when the historical particularity of style is dropped from the curriculum. That aesthetic exchange whereby writers strive to outdo their precursors (vividly traced by Harold Bloom and W. Jackson Bate) has taken a back seat to more socially pressing concerns. Although serious writers continue to write good books, interesting books, unusual books, literature itself is now viewed primarily as a cultural artifact defined by limitations of sex, race, and class. It acts more as a critique of society than as a gloss on previous work. To take a well-worn example: Joseph Conrad’s Heart of Darkness is invariably seen as a racial and colonialist work of fiction. No one is saying that the story is not well told. Nonetheless, it is bad in a way that is more important than the ways it is good.

More recently, the novelist Jesmyn Ward, writing in The New York Times Book Review, gracefully extolled the virtues of The Great Gatsby while focusing on Gatsby’s exclusionary status as though it were the novel’s most important feature: “the idea most invisible to [her] as a young reader [was] that the very social class that embodied the dream Gatsby wanted for himself was predicated on exclusion. That Gatsby was doomed from the start. He’d been born on the outside; he would die on the outside.” But what reader past the age of 14 doesn’t get this? It’s not that Ward is wrong; it’s just that harping on James Gatz’s displacement conveniently lines up with our culture’s need to condemn privilege.

I may be overreaching, but this emphasis on the socioeconomic aspect of the novel suggests that we’re in danger of losing a category of pleasure. If what is most important in a book is its attitude toward imperialism or class or injustice, then we automatically consign good writing to secondary status. Gatsby is great not because James Gatz is an interloper who exposes class prejudice, but because Fitzgerald learned from Conrad (as well as from Booth Tarkington, Sherwood Anderson, and Compton Mackenzie). Wanting to be a great writer, he had to be his own writer, and with Gatsby, he aspired to “write something new—something extraordinary and beautiful and simple + intricately patterned.” And part of the pleasure of reading Gatsby is discovering where and how he differed from the writers he admired.

]]></description>
<dc:subject>models-and-modes literary-criticism literature performance-measure social-construction-of-social-constructions to-write-about fads-and-well-not-really-fads-but-maybe-fallacies aesthetics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fc053e03bbb5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:literary-criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:literature"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:social-construction-of-social-constructions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fads-and-well-not-really-fads-but-maybe-fallacies"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aesthetics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/">
    <title>How Machine Learning Found Flint’s Lead Pipes - The Atlantic</title>
    <dc:date>2019-01-05T13:15:11+00:00</dc:date>
    <link>https://www.theatlantic.com/technology/archive/2019/01/how-machine-learning-found-flints-lead-pipes/578692/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[But something strange happened over the course of 2018: As more and more people had their pipes evaluated in 2018, fewer and fewer inspections were finding lead pipes. In November 2017, according to meeting notes obtained by local news outlet MLive’s Zahra Ahmad, the city’s head of public works estimated that about 10,000 of Flint’s homes still had lead pipes, roughly in line with the number other experts have floated. The new contractor hasn’t been efficiently locating those pipes: As of mid-December 2018, 10,531 properties had been explored and only 1,567 of those digs found lead pipes to replace. That’s a lead-pipe hit rate of just 15 percent, far below the 2017 mark.

]]></description>
<dc:subject>machine-learning planning public-policy models-and-modes civil-engineering failure-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8c668b395477/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:civil-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:failure-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1802.07029">
    <title>[1802.07029] On a fully fuzzy framework for minimax mixed integer linear programming</title>
    <dc:date>2018-12-11T13:05:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1802.07029</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this work, we present a modeling framework for minimax mixed 0-1 fuzzy linear problems. It is based on extending the usual rewriting of crisp minimax problems via auxiliary variables to model the maximum of a finite set of fuzzy linear functions. We establish that the considered problem can be equivalently formulated as a multiple objective mixed integer programming problem. The framework is applied to a fully fuzzy version of the capacitated center facility location problem.
]]></description>
<dc:subject>representation fuzzy operations-research integer-programming models-and-modes to-write-about could-be-clearer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:849a03a5f916/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fuzzy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:integer-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:could-be-clearer"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hackernoon.com/explainable-ai-wont-deliver-here-s-why-6738f54216be">
    <title>Explainable AI won’t deliver. Here’s why. – Hacker Noon</title>
    <dc:date>2018-12-11T12:36:06+00:00</dc:date>
    <link>https://hackernoon.com/explainable-ai-wont-deliver-here-s-why-6738f54216be</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Imagine choosing between two spaceships. Spaceship 1 comes with exact equations explaining how it works, but has never been flown. How Spaceship 2 flies is a mystery, but it has undergone extensive testing, with years of successful flights like the one you’re going on.
Which spaceship would you choose?
This is a philosophical question, so I can’t answer it for you. I know I have a personal preference — maybe that’s the statistician in me — but I would choose careful testing as a better basis for trust.
]]></description>
<dc:subject>artificial-intelligence explanation philosophy-of-engineering maintainability models-and-modes that-taco-bell-girl-says-what?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9e25d08a8416/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:maintainability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:that-taco-bell-girl-says-what?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://blogs.scientificamerican.com/observations/quantum-epistemology-for-business/">
    <title>Quantum Epistemology for Business - Scientific American Blog Network</title>
    <dc:date>2018-11-04T13:03:03+00:00</dc:date>
    <link>https://blogs.scientificamerican.com/observations/quantum-epistemology-for-business/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We are laboring under the illusion of “classical information” and “classical measurement” when we deal with human and organizational phenomena. Insights from quantum epistemology raise indelible doubts about the given-ness of data. But businesspeople need more than reasonable doubt: they need insights and action prompts. How can we leverage “quantum effects” in human organizations? The “quantum epistemology of social phenomena” is in its infancy, but already can provide a battery of new questions for those who want to understand and shape the process of measurement.
]]></description>
<dc:subject>planning define-your-terms rather-interesting models-and-modes to-write-about management</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fc719a1c21dc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:management"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rspb.royalsocietypublishing.org/content/282/1813/20151019">
    <title>THE EXTENDED EVOLUTIONARY SYNTHESIS | Proceedings of the Royal Society of London B: Biological Sciences</title>
    <dc:date>2018-10-01T11:37:15+00:00</dc:date>
    <link>http://rspb.royalsocietypublishing.org/content/282/1813/20151019</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Scientific activities take place within the structured sets of ideas and assumptions that define a field and its practices. The conceptual framework of evolutionary biology emerged with the Modern Synthesis in the early twentieth century and has since expanded into a highly successful research program to explore the processes of diversification and adaptation. Nonetheless, the ability of that framework satisfactorily to accommodate the rapid advances in developmental biology, genomics and ecology has been questioned. We review some of these arguments, focusing on literatures (evo-devo, developmental plasticity, inclusive inheritance and niche construction) whose implications for evolution can be interpreted in two ways—one that preserves the internal structure of contemporary evolutionary theory and one that points towards an alternative conceptual framework. The latter, which we label the ‘extended evolutionary synthesis' (EES), retains the fundaments of evolutionary theory, but differs in its emphasis on the role of constructive processes in development and evolution, and reciprocal portrayals of causation. In the EES, developmental processes, operating through developmental bias, inclusive inheritance and niche construction, share responsibility for the direction and rate of evolution, the origin of character variation and organism–environment complementarity. We spell out the structure, core assumptions and novel predictions of the EES, and show how it can be deployed to stimulate and advance research in those fields that study or use evolutionary biology.

]]></description>
<dc:subject>evolutionary-biology academic-culture models-and-modes theoretical-biology define-your-terms rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f098d09d586c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:academic-culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.10756">
    <title>[1809.10756] An Introduction to Probabilistic Programming</title>
    <dc:date>2018-10-01T11:16:12+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.10756</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This document is designed to be a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. 
We start with a discussion of model-based reasoning and explain why conditioning as a foundational computation is central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a simple first-order probabilistic programming language (PPL) whose programs define static-computation-graph, finite-variable-cardinality models. In the context of this restricted PPL we introduce fundamental inference algorithms and describe how they can be implemented in the context of models denoted by probabilistic programs. 
In the second part of this document, we introduce a higher-order probabilistic programming language, with a functionality analogous to that of established programming languages. This affords the opportunity to define models with dynamic computation graphs, at the cost of requiring inference methods that generate samples by repeatedly executing the program. Foundational inference algorithms for this kind of probabilistic programming language are explained in the context of an interface between program executions and an inference controller. 
This document closes with a chapter on advanced topics which we believe to be, at the time of writing, interesting directions for probabilistic programming research; directions that point towards a tight integration with deep neural network research and the development of systems for next-generation artificial intelligence applications.
]]></description>
<dc:subject>via:jar programming-language representation probability-theory models-and-modes semantics syntax pragmatics to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ac7a28a26463/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:jar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:semantics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:syntax"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/early/2018/09/19/421842">
    <title>A non-spatial account of place and grid cells based on clustering models of concept learning | bioRxiv</title>
    <dc:date>2018-09-20T11:38:37+00:00</dc:date>
    <link>https://www.biorxiv.org/content/early/2018/09/19/421842</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[One view is that conceptual knowledge is organized as a "cognitive map" in the brain, using the circuitry in the medial temporal lobe (MTL) that supports spatial navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so called place and grid cells emerge because of the relatively uniform sampling of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leads to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared.

]]></description>
<dc:subject>models-and-modes emergence data-analysis rather-interesting to-write-about consider:the-mangle</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:10d28be7bd95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:the-mangle"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://evonomics.com/the-only-woman-to-win-the-nobel-prize-economics-debunked/">
    <title>The Only Woman to Win the Nobel Prize in Economics Also Debunked the Orthodoxy - Evonomics</title>
    <dc:date>2018-05-16T12:30:53+00:00</dc:date>
    <link>http://evonomics.com/the-only-woman-to-win-the-nobel-prize-economics-debunked/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[I mention Lloyd’s essay to illustrate how ridiculous yet persistent the misconceptions about the “tragedy” dynamic truly are. Commons scholar Lewis Hyde dryly notes, “Just as Hardin proposes a herdsman whose reason is unable to encompass the common good, so Lloyd supposes persons who have no way to speak with each other or make joint decisions. Both writers inject laissez-faire individualism into an old agrarian village and then gravely announce that the commons is dead. From the point of view of such a village, Lloyd’s assumptions are as crazy as asking us to ‘suppose a man to have a purse to which his left and right hand may freely resort, each unaware of the other’.”

This absurdity, unfortunately, is the basis for a large literature of “prisoner’s dilemma” experiments that purport to show how “rational individuals” behave when confronted with “social dilemmas,” such as how to allocate a limited resource. Should the “prisoner” cooperate with other potential claimants and share the limited rewards? Or should he or she defect by grabbing as much for himself as possible?

]]></description>
<dc:subject>economics ideology public-policy models-and-modes commons to-write-about theory-and-practice-sitting-in-a-tree libertarianism assumptions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:84d60963032c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ideology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:commons"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theory-and-practice-sitting-in-a-tree"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:libertarianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:assumptions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://johncarlosbaez.wordpress.com/2018/04/27/props-in-network-theory/">
    <title>Props in Network Theory | Azimuth</title>
    <dc:date>2018-04-30T11:36:57+00:00</dc:date>
    <link>https://johncarlosbaez.wordpress.com/2018/04/27/props-in-network-theory/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We start with circuits made solely of ideal perfectly conductive wires. Then we consider circuits with passive linear components like resistors, capacitors and inductors. Finally we turn on the power and consider circuits that also have voltage and current sources.

And here’s the cool part: each kind of circuit corresponds to a prop that pure mathematicians would eventually invent on their own! So, what’s good for engineers is often mathematically natural too.

]]></description>
<dc:subject>network-theory abstraction rather-interesting models-and-modes circles-and-arrows bond-diagrams to-write-about to-understand functional-programming category-theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:076a282db228/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abstraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:circles-and-arrows"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bond-diagrams"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:functional-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:category-theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://psych-networks.com/meaning-model-equivalence-network-models-latent-variables-theoretical-space/">
    <title>The meaning of model equivalence: Network models, latent variables, and the theoretical space in between | Psych Networks</title>
    <dc:date>2018-04-02T11:37:04+00:00</dc:date>
    <link>http://psych-networks.com/meaning-model-equivalence-network-models-latent-variables-theoretical-space/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recently, an important set of equivalent representations of the Ising model was published by Joost Kruis and Gunter Maris in Scientific Reports. The paper constructs elegant representations of the Ising model probability distribution in terms of a network model (which consists of direct relations between observables), a latent variable model (which consists of relations between a latent variable and observables, in which the latent variable acts as a common cause), and a common effect model (which also consists of relations between a latent variable and observables, but here the latent variable acts as a common effect). The latter equivalence is a novel contribution to the literature and a quite surprising finding, because it means that a formative model can be statistically equivalent to a reflective model, which one may not immediately expect (do note that this equivalence need not maintain dimensionality, so a model with a single common effect may translate in a higher-dimensional latent variable model).

However, the equivalence between the ordinary (reflective) latent variable models and network models has been with us for a long time, and I therefore was rather surprised at some people’s reaction to the paper and the blog post that accompanies it. Namely, it appears that some think that (a) the fact that network structures can mimic reflective latent variables and vice versa is a recent discovery, that (b) somehow spells trouble for the network approach itself (because, well, what’s the difference?). The first of these claims is sufficiently wrong to go through the trouble of refuting it, if only to set straight the historical record; the second is sufficiently interesting to investigate it a little more deeply. Hence the following notes.

]]></description>
<dc:subject>dynamical-systems models models-and-modes representation philosophy-of-science (in-practice) to-write-about via:several</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7a32b57046ff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:(in-practice)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:several"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://lispcast.com/church-vs-curry-types/">
    <title>Church vs Curry Types - LispCast</title>
    <dc:date>2018-03-17T14:38:17+00:00</dc:date>
    <link>https://lispcast.com/church-vs-curry-types/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[My ambitious hope is that this perspective will quiet a lot of the fighting as people recognize that they are just perpetuating a rift in the field of mathematics that happened a long time ago. The perspectives are irreconcilable now, but that could change. A paper called Church and Curry: Combining Intrinsic and Extrinsic Typing builds a language with both kinds of types. And Gradual Typing and Blame Calculus are investigating the intersection of static and dynamic typing. Let’s stop fighting, make some cool tools and use them well.

]]></description>
<dc:subject>type-theory computer-science models-and-modes dichotomy-or-not? to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:21599c14588a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:type-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dichotomy-or-not?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mobile.nytimes.com/2018/02/28/opinion/corporate-america-suppressing-wages.html">
    <title>Opinion | Corporate America Is Suppressing Wages for Many Workers - The New York Times</title>
    <dc:date>2018-03-17T14:23:43+00:00</dc:date>
    <link>https://mobile.nytimes.com/2018/02/28/opinion/corporate-america-suppressing-wages.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[For a long time, economists believed that labor-market monopsony rarely existed, at least outside old-fashioned company towns where a single factory employs most of the residents. But in recent decades, several compelling studies have revealed that monopsony is omnipresent. Professionals like doctors and nurses, workers in factories and meat processing plants, and sandwich makers and other low-skill workers earn far less — thousands of dollars less — than they would if employers did not dominate labor markets.

The studies show that common features of the labor market give enormous bargaining advantages to employers. Because most people sink roots in their communities, they are reluctant to quit their job and move to a job that is far away. Because workplaces differ in terms of their location and conditions, people have trouble comparing them, which means that one cannot easily “comparison shop” for jobs. And thanks to a wave of consolidation, industries are increasingly dominated by a small number of huge companies, which means that workers have fewer choices among employers in their area.

When employers exercise monopsonistic power, wages are suppressed, jobs are left unfilled, and economic growth suffers. Unions used to offset employer monopsony power, but unions now represent only 7 percent of private sector workers, down from a peak of 35 percent in the 1950s. Combating the practices that employers use to monopsonize the labor market can lead to higher wages, more jobs and faster economic growth.

]]></description>
<dc:subject>worklife economics models-and-modes public-policy power-relations to-write-about capitalism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f3857329eb29/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:worklife"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:public-policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:power-relations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:capitalism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1802.02627">
    <title>[1802.02627] Going Deeper in Spiking Neural Networks: VGG and Residual Architectures</title>
    <dc:date>2018-02-11T16:02:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1802.02627</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.
]]></description>
<dc:subject>via:? to-write-about to-read neural-networks representation models-and-modes machine-learning simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f562d552667c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1703.10651">
    <title>[1703.10651] Reliable Decision Support using Counterfactual Models</title>
    <dc:date>2018-01-28T12:28:37+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.10651</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Making a good decision involves considering the likely outcomes under each possible action. For example, would drug A or drug B lead to a better outcome for this patient? Ideally, we answer these questions using an experiment, but this is not always possible (e.g., it may be unethical). As an alternative, we can use non-experimental data to learn models that make counterfactual predictions of what we would observe had we run an experiment. To learn such models for decision-making problems, we propose the use of counterfactual objectives in lieu of classical supervised learning objectives. We implement this idea in a challenging and frequently occurring context, and propose the counterfactual GP (CGP), a counterfactual model of continuous-time trajectories (time series) under sequences of actions taken in continuous-time. We develop our model within the potential outcomes framework of Neyman and Rubin. The counterfactual GP is trained using a joint maximum likelihood objective that adjusts for dependencies between observed actions and outcomes in the training data. We report two sets of experimental results. First, we show that the CGP's predictions are reliable; they are stable to changes in certain characteristics of the training data that are not relevant to the decision-making problem. Predictive models trained using classical supervised learning objectives, however, are not stable to such perturbations. In the second experiment, we use data from a real intensive care unit (ICU) and qualitatively demonstrate how the CGP's ability to answer "What if?" questions offers medical decision-makers a powerful new tool for planning treatment.
]]></description>
<dc:subject>machine-learning models-and-modes rather-interesting to-write-about consider:symbolic-regression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3dab5493d8cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:symbolic-regression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1710.03453">
    <title>[1710.03453] The Sparse Multivariate Method of Simulated Quantiles</title>
    <dc:date>2017-11-27T12:13:10+00:00</dc:date>
    <link>https://arxiv.org/abs/1710.03453</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper the method of simulated quantiles (MSQ) of Dominicy and Veredas (2013) and Dominick et al. (2013) is extended to a general multivariate framework (MMSQ) and to provide a sparse estimator of the scale matrix (sparse-MMSQ). The MSQ, like alternative likelihood-free procedures, is based on the minimisation of the distance between appropriate statistics evaluated on the true and synthetic data simulated from the postulated model. Those statistics are functions of the quantiles providing an effective way to deal with distributions that do not admit moments of any order like the α-Stable or the Tukey lambda distribution. The lack of a natural ordering represents the major challenge for the extension of the method to the multivariate framework. Here, we rely on the notion of projectional quantile recently introduced by Hallin etal. (2010) and Kong Mizera (2012). We establish consistency and asymptotic normality of the proposed estimator. The smoothly clipped absolute deviation (SCAD) ℓ1--penalty of Fan and Li (2001) is then introduced into the MMSQ objective function in order to achieve sparse estimation of the scaling matrix which is the major responsible for the curse of dimensionality problem. We extend the asymptotic theory and we show that the sparse-MMSQ estimator enjoys the oracle properties under mild regularity conditions. The method is illustrated and its effectiveness is tested using several synthetic datasets simulated from the Elliptical Stable distribution (ESD) for which alternative methods are recognised to perform poorly. The method is then applied to build a new network-based systemic risk measurement framework. The proposed methodology to build the network relies on a new systemic risk measure and on a parametric test of statistical dominance.
]]></description>
<dc:subject>statistics reinventing-the-wheel how-is-this-not-constrained-symbolic-regression? algorithms models-and-modes to-understand inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ee7c13381df0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinventing-the-wheel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:how-is-this-not-constrained-symbolic-regression?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1703.04977">
    <title>[1703.04977] What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?</title>
    <dc:date>2017-05-07T11:12:57+00:00</dc:date>
    <link>https://arxiv.org/abs/1703.04977</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.
]]></description>
<dc:subject>computer-vision machine-learning models-and-modes uncertainty deep-learning rather-interesting define-your-terms representation nudge-targets to-wrt</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:84593bb3c4f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-vision"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-wrt"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1312.7604">
    <title>[1312.7604] Probabilistic Archetypal Analysis</title>
    <dc:date>2017-02-27T00:56:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1312.7604</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.
]]></description>
<dc:subject>machine-learning dimension-reduction rather-interesting models-and-modes to-understand to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:61cdc14b9b1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dimension-reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://thearchdruidreport.blogspot.com/2017/02/perched-on-wheel-of-time.html">
    <title>The Archdruid Report: Perched on the Wheel of Time</title>
    <dc:date>2017-02-19T13:54:28+00:00</dc:date>
    <link>http://thearchdruidreport.blogspot.com/2017/02/perched-on-wheel-of-time.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In the final chapters of his second volume, for example, Spengler noted that civilizations in the stage ours was about to reach always end up racked by conflicts that pit established hierarchies against upstart demagogues who rally the disaffected and transform them into a power base. Looking at the trends visible in his own time, he sketched out the most likely form those conflicts would take in the Winter phase of our civilization. Modern representative democracy, he pointed out, has no effective defenses against corruption by wealth, and so could be expected to evolve into corporate-bureaucratic plutocracies that benefit the affluent at the expense of everyone else. Those left out in the cold by these transformations, in turn, end up backing what Spengler called Caesarism—the rise of charismatic demagogues who challenge and eventually overturn the corporate-bureaucratic order.

These demagogues needn’t come from within the excluded classes, by the way. Julius Caesar, the obvious example, came from an old upper-class Roman family and parlayed his family connections into a successful political career. Watchers of the current political scene may be interested to know that Caesar during his lifetime wasn’t the imposing figure he became in retrospect; he had a high shrill voice, his morals were remarkably flexible even by Roman standards—the scurrilous gossip of his time called him “every man’s wife and every woman’s husband”—and he spent much of his career piling up huge debts and then wriggling out from under them. Yet he became the political standardbearer for the plebeian classes, and his assassination by a conspiracy of rich Senators launched the era of civil wars that ended the rule of the old elite once and for all.
]]></description>
<dc:subject>history political-economy philosophy models-and-modes argumentation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:24f132e1f310/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:political-economy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:argumentation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1612.02483">
    <title>[1612.02483] High Dimensional Consistent Digital Segments</title>
    <dc:date>2017-01-10T13:30:15+00:00</dc:date>
    <link>https://arxiv.org/abs/1612.02483</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of digitalizing Euclidean line segments from ℝd to ℤd. Christ {\em et al.} (DCG, 2012) showed how to construct a set of {\em consistent digital segment} (CDS) for d=2: a collection of segments connecting any two points in ℤ2 that satisfies the natural extension of the Euclidean axioms to ℤd. In this paper we study the construction of CDSs in higher dimensions. 
We show that any total order can be used to create a set of {\em consistent digital rays} CDR in ℤd (a set of rays emanating from a fixed point p that satisfies the extension of the Euclidean axioms). We fully characterize for which total orders the construction holds and study their Hausdorff distance, which in particular positively answers the question posed by Christ {\em et al.}.
]]></description>
<dc:subject>approximation computational-geometry performance-measure rather-interesting mathematics consistency models-and-modes constructive-geometry nudge-targets consider:representation consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e655441af06c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consistency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constructive-geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1508.05837">
    <title>[1508.05837] Hydroassets Portfolio Management for Intraday Electricity Trading in a Discrete Time Stochastic Optimization Perspective</title>
    <dc:date>2017-01-08T14:32:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1508.05837</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Hydro storage system optimization is becoming one of the most challenging task in Energy Finance. Following the Blomvall and Lindberg (2002) interior point model, we set up a stochastic multiperiod optimization procedure by means of a "bushy" recombining tree that provides fast computational results. Inequality constraints are packed into the objective function by the logarithmic barrier approach and the utility function is approximated by its second order Taylor polynomial. The optimal solution for the original problem is obtained as a diagonal sequence where the first diagonal dimension is the parameter controlling the logarithmic penalty and the second is the parameter for the Newton step in the construction of the approximated solution. Optimal intraday electricity trading and water values for hydroassets as shadow prices are computed. The algorithm is implemented in Mathematica.
]]></description>
<dc:subject>portfolio-theory operations-research financial-engineering time-series prediction models-and-modes nudge-targets consider:performance-measures consider:metaheuristics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6055ba5c2345/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:portfolio-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:financial-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:time-series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:metaheuristics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1604.04647">
    <title>[1604.04647] Sheaf and duality methods for analyzing multi-model systems</title>
    <dc:date>2017-01-07T14:13:03+00:00</dc:date>
    <link>https://arxiv.org/abs/1604.04647</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There is an interplay between models, specified by variables and equations, and their connections to one another. This dichotomy should be reflected in the abstract as well. Without referring to the models directly -- only that a model consists of spaces and maps between them -- the most readily apparent feature of a multi-model system is its topology. We propose that this topology should be modeled first, and then the spaces and maps of the individual models be specified in accordance with the topology. Axiomatically, this construction leads to sheaves. Sheaf theory provides a toolbox for constructing predictive models described by systems of equations. Sheaves are mathematical objects that manage the combination of bits of local information into a consistent whole. The power of this approach is that complex models can be assembled from smaller, easier-to-construct models. The models discussed in this chapter span the study of continuous dynamical systems, partial differential equations, probabilistic graphical models, and discrete approximations of these models.
]]></description>
<dc:subject>category-theory to-understand models-and-modes rather-interesting no-really-I-think-I-need-to-understand-this-thread</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:82e1035d6e8f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:category-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:no-really-I-think-I-need-to-understand-this-thread"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biorxiv.org/content/early/2016/12/23/096438?rss=1%2522">
    <title>Genotypic complexity of Fisher's geometric model | bioRxiv</title>
    <dc:date>2017-01-04T13:25:01+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2016/12/23/096438?rss=1%2522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Fisher's geometric model was originally introduced to argue that complex adaptations must occur in small steps because of pleiotropic constraints. When supplemented with the assumption of additivity of mutational effects on phenotypic traits, it provides a simple mechanism for the emergence of genotypic epistasis from the nonlinear mapping of phenotypes to fitness. Of particular interest is the occurrence of sign epistasis, which is a necessary condition for multipeaked genotypic fitness landscapes. Here we compute the probability that a pair of randomly chosen mutations interacts sign-epistatically, which is found to decrease algebraically with increasing phenotypic dimension n, and varies non-monotonically with the distance from the phenotypic optimum. We then derive asymptotic expressions for the mean number of fitness maxima in genotypic landscapes composed of all combinations of L random mutations. This number increases exponentially with L, and the corresponding growth rate is used as a measure of the complexity of the genotypic landscape. The dependence of the complexity on the parameters of the model is found to be surprisingly rich, and three distinct phases characterized by different landscape structures are identified. The complexity generally decreases with increasing phenotypic dimension, but a non-monotonic dependence on n is found in certain regimes. Our results inform the interpretation of experiments where the parameters of Fisher's model have been inferred from data, and help to elucidate which features of empirical fitness landscapes can (or cannot) be described by this model.

]]></description>
<dc:subject>population-biology theoretical-biology theory-and-practice-sitting-in-a-tree fitness-landscapes models-and-modes to-write-about nudge-targets consider:rediscovery consider:robustness consider:multiobjective-versions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:02ca5aad3afb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:population-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theory-and-practice-sitting-in-a-tree"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:multiobjective-versions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1612.02540">
    <title>[1612.02540] City traffic forecasting using taxi GPS data: A coarse-grained cellular automata model</title>
    <dc:date>2016-12-17T13:49:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1612.02540</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[City traffic is a dynamic system of enormous complexity. Modeling and predicting city traffic flow remains to be a challenge task and the main difficulties are how to specify the supply and demands and how to parameterize the model. In this paper we attempt to solve these problems with the help of large amount of floating car data. We propose a coarse-grained cellular automata model that simulates vehicles moving on uniform grids whose size are much larger compared with the microscopic cellular automata model. The car-car interaction in the microscopic model is replaced by the coupling between vehicles and coarse-grained state variables in our model. To parameterize the model, flux-occupancy relations are fitted from the historical data at every grids, which serve as the coarse-grained fundamental diagrams coupling the occupancy and speed. To evaluate the model, we feed it with the historical travel demands and trajectories obtained from the floating car data and use the model to predict road speed one hour into the future. Numerical results show that our model can capture the traffic flow pattern of the entire city and make reasonable predictions. The current work can be considered a prototype for a model-based forecasting system for city traffic.
]]></description>
<dc:subject>agent-based cellular-automata rather-interesting traffic models-and-modes to-write-about representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83f11647af4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:traffic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.researchgate.net/publication/301698580_On_Computational_Explanations_Synthese_in_press_DOI_101007s11229-016-1101-5">
    <title>On Computational Explanations/ Synthese (in press) DOI 10.1007/s11229-016-1101-5 (PDF Download Available)</title>
    <dc:date>2016-12-09T12:30:51+00:00</dc:date>
    <link>https://www.researchgate.net/publication/301698580_On_Computational_Explanations_Synthese_in_press_DOI_101007s11229-016-1101-5</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: (1) What type of explanations they are, (2) in what sense computational explanations are explanatory and (3) to what extent they involve a special, “independent” or “autonomous” level of explanation. In this paper, we defend the view computational explanations are genuine explanations, which track non-causal/formal dependencies. Specifically, we argue that they do not provide mere sketches for explanation, in contrast to what for example Piccinini and Craver (Synthese 183(3):283–311, 2011) suggest. This view of computational explanations implies some degree of “autonomy” for the computational level. However, as we will demonstrate that does not make this view “computationally chauvinistic” in a way that Piccinini (Synthese 153:343–353, 2006b) or Kaplan (Synthese 183(3):339–373, 2011) have charged it to be.
]]></description>
<dc:subject>via:cshalizi philosophy-of-science explanation models-and-modes to-read to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:21bb4d22c4f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:explanation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1608.05226">
    <title>[1608.05226] A tale of a Principal and many many Agents</title>
    <dc:date>2016-08-31T21:20:15+00:00</dc:date>
    <link>http://arxiv.org/abs/1608.05226</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we investigate a moral hazard problem in finite time with lump-sum and continuous payments, involving infinitely many Agents, with mean field type interactions, hired by one Principal. By reinterpreting the mean-field game faced by each Agent in terms of a mean field FBSDE, we are able to rewrite the Principal's problem as a control problem for McKean-Vlasov SDEs. We review two general approaches to tackle it: the first one introduced recently in [2, 66, 67, 68, 69] using dynamic programming and Hamilton-Jacobi-Bellman equations, the second based on the stochastic Pontryagin maximum principle, which follows [16]. We solve completely and explicitly the problem in special cases, going beyond the usual linear-quadratic framework. We finally show in our examples that the optimal contract in the N-players' model converges to the mean-field optimal contract when the number of agents goes to +∞.
]]></description>
<dc:subject>probability-theory options optimization models-and-modes rather-interesting agent-based nudge-targets consider:looking-to-see to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:56c8264512b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:options"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1607.06274">
    <title>[1607.06274] Topological Data Analysis with Bregman Divergences</title>
    <dc:date>2016-08-15T12:31:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1607.06274</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Given a finite set in a metric space, the topological analysis generalizes hierarchical clustering using a 1-parameter family of homology groups to quantify connectivity in all dimensions. The connectivity is compactly described by the persistence diagram. One limitation of the current framework is the reliance on metric distances, whereas in many practical applications objects are compared by non-metric dissimilarity measures. Examples are the Kullback-Leibler divergence, which is commonly used for comparing text and images, and the Itakura-Saito divergence, popular for speech and sound. These are two members of the broad family of dissimilarities called Bregman divergences. 
We show that the framework of topological data analysis can be extended to general Bregman divergences, widening the scope of possible applications. In particular, we prove that appropriately generalized Cech and Delaunay (alpha) complexes capture the correct homotopy type, namely that of the corresponding union of Bregman balls. Consequently, their filtrations give the correct persistence diagram, namely the one generated by the uniformly growing Bregman balls. Moreover, we show that unlike the metric setting, the filtration of Vietoris-Rips complexes may fail to approximate the persistence diagram. We propose algorithms to compute the thus generalized Cech, Vietoris-Rips and Delaunay complexes and experimentally test their efficiency. Lastly, we explain their surprisingly good performance by making a connection with discrete Morse theory.]]></description>
<dc:subject>data-analysis topology metrics to-understand algorithms representation statistics probability-theory models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6f2fa3f0f89f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:topology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1606.03490#">
    <title>[1606.03490] The Mythos of Model Interpretability</title>
    <dc:date>2016-07-05T11:55:48+00:00</dc:date>
    <link>https://arxiv.org/abs/1606.03490#</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.
]]></description>
<dc:subject>models-and-modes infighting representation philosophy-of-engineering to-write-about neural-networks cultural-assumptions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:94a57fc5464c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:infighting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cultural-assumptions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1604.01674">
    <title>[1604.01674] OFFl models: novel schema for dynamical modeling of biological systems</title>
    <dc:date>2016-07-01T21:09:52+00:00</dc:date>
    <link>http://arxiv.org/abs/1604.01674</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of "ODEs and formalized flow diagrams" as OFFl.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler's behalf. In this report, we describe the chief motivations for OFFl, outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.
]]></description>
<dc:subject>models-and-modes representation visualization systems-biology formalization amusing theoretical-biology systems-thinking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c4e23570d367/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:amusing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-thinking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1601.03243">
    <title>[1601.03243] Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli</title>
    <dc:date>2016-06-29T11:17:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1601.03243</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.
]]></description>
<dc:subject>cell-biology theoretical-biology rather-interesting nonlinear-dynamics models-and-modes stochastic-systems probability-theory nudge-targets consider:robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:02b52a07b334/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1505.01396">
    <title>[1505.01396] A common brew - The relationships between varieties of percolation</title>
    <dc:date>2016-05-08T12:36:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.01396</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In recent years, many variants of percolation have been used to study the structure of networks and the behavior of processes spreading along those networks. Among these are bond percolation, site percolation, k-core percolation, bootstrap percolation, the generalized epidemic process, and the Watts Threshold Model (WTM). We show that --- except for bond percolation --- each of these processes can be derived as a special case of the WTM. In fact "heterogeneous k-core percolation", a corresponding "heterogeneous bootstrap percolation" model, and the generalized epidemic process are in fact equivalent to one another and the WTM. We further show that a natural generalization of the WTM in which individuals "transmit" or "send a message" to their neighbors with some probability less than 1 can be reformulated in terms of the WTM, and so this apparent generalization is in fact not more general. Finally, we show that in bond percolation, finding the set of nodes in the component containing a given node is equivalent to finding the set of nodes activated if that node is initially activated and the node thresholds are chosen from the appropriate distribution. A consequence of these results is that mathematical techniques developed for the WTM apply to these other models as well, and techniques that were developed for some particular case may in fact apply much more generally.
]]></description>
<dc:subject>percolation models-and-modes horse-races comparison rather-interesting simulation performance-network nudge-targets consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6a18f8235205/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:percolation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:comparison"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-network"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1604.08412">
    <title>[1604.08412] Probabilistic Foundations of Contextuality</title>
    <dc:date>2016-05-01T12:18:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1604.08412</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Contextuality is usually defined as absence of a joint distribution for a set of measurements (random variables) with known joint distributions of some of its subsets. However, if these subsets of measurements are not disjoint, contextuality is mathematically impossible even if one generally allows (as one must) for random variables not to be jointly distributed. To avoid contradictions one has to adopt the Contextuality-by-Default approach: measurements made in different contexts are always distinct and stochastically unrelated to each other. Contextuality is reformulated then in terms of the (im)possibility of imposing on all the measurements in a system a joint distribution of a particular kind: such that any measurements of one and the same property made in different contexts satisfy a specified property, . In the traditional analysis of contextuality  means "are equal to each other with probability 1". However, if the system of measurements violates the "no-disturbance principle", due to signaling or experimental biases, then the meaning of  has to be generalized, and the proposed generalization is "are equal to each other with maximal possible probability" (applied to any set of measurements of one and the same property). This approach is illustrated on arbitrary systems of binary measurements, including most of quantum systems of traditional interest in contextuality studies (irrespective of whether "no-disturbance" principle holds in them).
]]></description>
<dc:subject>probability-theory philosophy-of-science quantums models-and-modes to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3e219b0b63ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantums"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1603.00984">
    <title>[1603.00984] David vs Goliath (You against the Markets), A Dynamic Programming Approach to Separate the Impact and Timing of Trading Costs</title>
    <dc:date>2016-05-01T12:15:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1603.00984</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[To trade, or not to trade, that is the question 
Whether an optimizer can yield the answer 
Against the spikes and crashes of markets gone wild. 
To quench one's thirst before liquidity runs dry 
Or wait till the tide of momentum turns mild. 
A trader's conundrum is whether (and how much) to trade during a given interval or wait for the next interval when the price momentum is more favorable to his direction of trading. We develop a fundamentally different stochastic dynamic programming model of trading costs based on the Bellman principle of optimality. We use this model to provide insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their actions) and Market Timing (price move caused by everyone else) components. Plugging different distributions of prices and volumes into this framework can help traders decide when to bear higher Market Impact by trading more in the hope of offsetting the cost of trading at a higher price later. We derive formulations of this model under different laws of motion of the security prices. We start with a benchmark scenario and extend this to include multiple sources of uncertainty, liquidity constraints due to volume curve shifts and relate trading costs to the spread.
]]></description>
<dc:subject>financial-engineering markets statistics models-and-modes rather-interesting uncertainty algorithms nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ed4ddbf195e6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:financial-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:markets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1512.07450">
    <title>[1512.07450] Interacting Behavior and Emerging Complexity</title>
    <dc:date>2016-03-20T16:27:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1512.07450</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Can we quantify the change of complexity throughout evolutionary processes? We attempt to address this question through an empirical approach. In very general terms, we simulate two simple organisms on a computer that compete over limited available resources. We implement Global Rules that determine the interaction between two Elementary Cellular Automata on the same grid. Global Rules change the complexity of the state evolution output which suggests that some complexity is intrinsic to the interaction rules themselves. The largest increases in complexity occurred when the interacting elementary rules had very little complexity, suggesting that they are able to accept complexity through interaction only. We also found that some Class 3 or 4 CA rules are more fragile than others to Global Rules, while others are more robust, hence suggesting some intrinsic properties of the rules independent of the Global Rule choice. We provide statistical mappings of Elementary Cellular Automata exposed to Global Rules and different initial conditions onto different complexity classes.
]]></description>
<dc:subject>cellular-automata Wolframism evolutionary-algorithms theoretical-biology complexology rather-interesting models-and-modes nudge-targets consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0723fa20534a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-automata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Wolframism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1510.05574">
    <title>[1510.05574] Physics of base-pairing dynamics in DNA</title>
    <dc:date>2016-02-24T12:45:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1510.05574</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As a key molecule of Life, Deoxyribonucleic acid (DNA) is the focus of numbers of investigations with the help of biological, chemical and physical techniques. From a physical point of view, both experimental and theoretical works have brought quantitative insights into DNA base-pairing dynamics that we review in this Report, putting emphasis on theoretical developments. We discuss the dynamics at the base-pair scale and its pivotal coupling with the polymer one, with a polymerization index running from a few nucleotides to tens of kilo-bases. This includes opening and closure of short hairpins and oligomers as well as zipping and unwinding of long macromolecules. We review how different physical mechanisms are either used by Nature or utilized in biotechnological processes to separate the two intertwined DNA strands, by insisting on quantitative results. They go from thermally-assisted denaturation bubble nucleation to force- or torque- driven mechanisms. We show that the helical character of the molecule, possibly supercoiled, can play a key role in many denaturation and renaturation processes. We categorize the mechanisms according to the relative timescales associated with base-pairing and chain degrees of freedom such as bending and torsional elastic ones. In some specific situations, these chain degrees of freedom can be integrated out, and the quasi- static approximation is valid. The complex dynamics then reduces to the diffusion in a low-dimensional free-energy landscape. In contrast, some important cases of experimental interest necessarily appeal to far-from-equilibrium statistical mechanics and hydrodynamics.]]></description>
<dc:subject>biophysics Peyrard-Bishoqp-Dauxois it's-more-complicated-than-you-think bioinformatics-ain't-databases models-and-modes to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1b0caba0bd0b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Peyrard-Bishoqp-Dauxois"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics-ain't-databases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1508.05990">
    <title>[1508.05990] A Multi-Time-Scale Analysis of Chemical Reaction Networks : II. Stochastic Systems</title>
    <dc:date>2016-02-09T17:48:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1508.05990</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider stochastic descriptions of chemical reaction networks in which there are both fast and slow reactions, and for which the time scales are widely separated. We develop a computational algorithm that produces the generator of the full chemical master equation for arbitrary systems, and show how to obtain a reduced equation that governs the evolution on the slow time scale. This is done by applying a state space decomposition to the full equation that leads to the reduced dynamics in terms of certain projections and the invariant distributions of the fast system. The rates or propensities of the reduced system are shown to be the rates of the slow reactions conditioned on the expectations of fast steps. We also show that the generator of the reduced system is a Markov generator, and we present an efficient stochastic simulation algorithm for the slow time scale dynamics. We illustrate the numerical accuracy of the approximation by simulating several examples. Graph-theoretic techniques are used throughout to describe the structure of the reaction network and the state-space transitions accessible under the dynamics.
]]></description>
<dc:subject>reaction-networks dynamical-systems simulation models-and-modes modeling-is-not-mathematics theoretical-biology bioinformatics nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:854bafa52318/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling-is-not-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>