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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://elevanth.org/blog/2023/07/17/none-of-the-above/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2305.08429"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2012.12670"/>
	<rdf:li rdf:resource="https://journals.sagepub.com/doi/abs/10.1177/0049124120914933"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1909.06523"/>
	<rdf:li rdf:resource="https://amstat.tandfonline.com/doi/full/10.1080/10618600.2019.1637749"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.00882"/>
	<rdf:li rdf:resource="https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12393?af=R"/>
	<rdf:li rdf:resource="http://nostalgebraist.tumblr.com/post/161645122124/bayes-a-kinda-sorta-masterpost"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1511.01844"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/113/34/9569.abstract.html"/>
	<rdf:li rdf:resource="https://ndpr.nd.edu/news/59657-reconstructing-reality-models-mathematics-and-simulations/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1412.3442"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1407.0050"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/111/33/11973.full"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.0302"/>
	<rdf:li rdf:resource="http://dx.doi.org/10.1214/13-BA024"/>
	<rdf:li rdf:resource="http://users.soe.ucsc.edu/~draper/draper-krnjajic-draft-2011.pdf"/>
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	<rdf:li rdf:resource="http://homepages.cwi.nl/~pdg/ftp/alt12longer.pdf"/>
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	<rdf:li rdf:resource="http://www.wordsmatter.caltech.edu/SSPapers/sswp1320.pdf"/>
	<rdf:li rdf:resource="http://rochester.edu/college/psc/clarke/POPArticle.pdf"/>
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	<rdf:li rdf:resource="http://delong.typepad.com/sdj/2011/10/calibration-and-econometric-non-practice.html?"/>
	<rdf:li rdf:resource="http://mpra.ub.uni-muenchen.de/34117/"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1106.2895"/>
	<rdf:li rdf:resource="http://philsci-archive.pitt.edu/8616/"/>
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	<rdf:li rdf:resource="http://www.people.fas.harvard.edu/~pgs/InductionSamplesKinds_INPC_final.pdf"/>
	<rdf:li rdf:resource="http://www.people.fas.harvard.edu/~pgs/PGS-StrategyMBS-06.pdf"/>
	<rdf:li rdf:resource="http://www.people.fas.harvard.edu/~pgs/PGSonPopper.pdf"/>
	<rdf:li rdf:resource="http://www.journals.uchicago.edu/doi/abs/10.1086/656009"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.3868"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.5483"/>
	<rdf:li rdf:resource="http://www.pitt.edu/~jdnorton/papers/material.pdf"/>
	<rdf:li rdf:resource="http://philsci-archive.pitt.edu/archive/00001446/"/>
	<rdf:li rdf:resource="http://psycnet.apa.org/journals/rev/107/2/358/"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1001.4656"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/0912.4269"/>
	<rdf:li rdf:resource="http://www.jstor.org/pss/193077"/>
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	<rdf:li rdf:resource="http://www.jstor.org/stable/2982519"/>
	<rdf:li rdf:resource="http://www.kellogg.northwestern.edu/faculty/weinstein/htm/learnability0208.pdf"/>
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  </channel><item rdf:about="http://computationalculture.net/situating-bayesian-knowledge/">
    <title>Situating Bayesian Knowledge: A Case Study of Modelling Pollutant Transfers from Land to Water – Computational Culture</title>
    <dc:date>2025-07-17T14:36:14+00:00</dc:date>
    <link>http://computationalculture.net/situating-bayesian-knowledge/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian statistics is an alternative to classical, frequentist statistics. Some have argued that the Bayesian framework embodies a necessarily ‘subjective’ perspective which accounts for the context dependence and relativity of knowledge and contrasts it with a frequentist approach. We argue that such epistemological discussions can actually obscure all the ways in which Bayesian knowledge is partial and, in this way, similar to frequentist knowledge. In this contribution, we explore the contingency and performativity of knowing in Bayesian ways by revisiting an application of Bayesian modelling in a case of pollutant transfers from land to water. We query this material from an STS perspective, thinking through a concrete Bayesian modelling process, the various choices made and their alternatives. We ponder how specific practices that play out in Bayesian modelling–model building, data preparation, setting the prior, defining the likelihood function, sampling from the posterior, and checking the model–produce knowledges that can be situated within and produce, for example, partial perspectives on the issue in question, on knowledge and the good, and within social and material contexts. We touch upon discussions on mathematical affordances of Bayesian modelling such as the lack of a built-in mechanism for updating the space of models. Ultimately, we discuss how Bayesian modelling practices enact aspects of the world, including ‘natural’, ‘social’, ‘political’ and ‘ethical’ ‘objects’, and can (re)configure (social) relations. We demonstrate the value of collaborating with actors that can unsettle the Bayesian workflow to iteratively preserve onto-epistemic openings."

--- I may need AEO to help me understand this...]]></description>
<dc:subject>to:NB to_read to_read_maybe the_french_disease bayesianism hydrology spatio-temporal_statistics re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e7e6be4024ca/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read_maybe"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_french_disease"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
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<item rdf:about="https://elevanth.org/blog/2023/07/17/none-of-the-above/">
    <title>None of the Above | Elements of Evolutionary Anthropology</title>
    <dc:date>2025-03-23T17:17:45+00:00</dc:date>
    <link>https://elevanth.org/blog/2023/07/17/none-of-the-above/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- If I am honest with myself, incorporating something like this (or even my own paper with Gelman!) into undergrad ADA would require a big re-design of the course, because it's currently "here is an array of sometimes-useful statistical methods", not "here is how you turn scientific questions into data-analytic problems, and statistical solutions back into scientific answers".  Knowing a lot of methods is _helpful_ to that undertaking, but it's different.  Maybe that's too much to ask of an undergrad class with >200 students/year...]]></description>
<dc:subject>statistics data_analysis have_read mcelreath.richard closing_old_tabs re:phil-of-bayes_paper to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1079612a0b01/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
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<item rdf:about="https://arxiv.org/abs/2305.08429">
    <title>[2305.08429] Bayesian inference for misspecified generative models</title>
    <dc:date>2023-06-08T15:32:21+00:00</dc:date>
    <link>https://arxiv.org/abs/2305.08429</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian inference is a powerful tool for combining information in complex settings, a task of increasing importance in modern applications. However, Bayesian inference with a flawed model can produce unreliable conclusions. This review discusses approaches to performing Bayesian inference when the model is misspecified, where by misspecified we mean that the analyst is unwilling to act as if the model is correct. Much has been written about this topic, and in most cases we do not believe that a conventional Bayesian analysis is meaningful when there is serious model misspecification. Nevertheless, in some cases it is possible to use a well-specified model to give meaning to a Bayesian analysis of a misspecified model and we will focus on such cases. Three main classes of methods are discussed - restricted likelihood methods, which use a model based on a non-sufficient summary of the original data; modular inference methods which use a model constructed from coupled submodels and some of the submodels are correctly specified; and the use of a reference model to construct a projected posterior or predictive distribution for a simplified model considered to be useful for prediction or interpretation."]]></description>
<dc:subject>to:NB bayesianism misspecification statistics re:phil-of-bayes_paper to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:683fd1cb974b/</dc:identifier>
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<item rdf:about="https://philpapers.org/rec/WHITEA">
    <title>Roger White, The epistemic advantage of prediction over accommodation - PhilPapers</title>
    <dc:date>2023-02-24T03:36:01+00:00</dc:date>
    <link>https://philpapers.org/rec/WHITEA</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["According to the thesis of Strong Predictionism, we typically have stronger evidence for a theory if it was used to predict certain data, than if it was deliberately constructed to accommodate those same data, even if we fully grasp the theory and all the evidence on which it was based. This thesis faces powerful objections and the existing arguments in support of it are seriously flawed. I offer a new defence of Strong Predictionism which overcomes the objections and provides a deeper understanding of the epistemic importance of prediction. I conclude by applying this account to strategies for defending scientific realism."]]></description>
<dc:subject>to:NB prediction philosophy_of_science re:phil-of-bayes_paper via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7dd0298aa71e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
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<item rdf:about="https://arxiv.org/abs/2012.12670">
    <title>[2012.12670] Testing whether a Learning Procedure is Calibrated</title>
    <dc:date>2020-12-24T15:34:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12670</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A learning procedure takes as input a dataset and performs inference for the parameters θ of a model that is assumed to have given rise to the dataset. Here we consider learning procedures whose output is a probability distribution, representing uncertainty about θ after seeing the dataset. Bayesian inference is a prime example of such a procedure but one can also construct other learning procedures that return distributional output. This paper studies conditions for a learning procedure to be considered calibrated, in the sense that the true data-generating parameters are plausible as samples from its distributional output. A learning procedure that is calibrated need not be statistically efficient and vice versa. A hypothesis-testing framework is developed in order to assess, using simulation, whether a learning procedure is calibrated. Finally, we exploit our framework to test the calibration of some learning procedures that are motivated as being approximations to Bayesian inference but are nevertheless widely used."]]></description>
<dc:subject>computational_statistics calibration model_checking approximate_bayesian_computation re:phil-of-bayes_paper in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a1543c3f9399/</dc:identifier>
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<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/0049124120914933">
    <title>Model Adequacy Checking/Goodness-of-fit Testing for Behavior in Joint Dynamic Network/Behavior Models, with an Extension to Two-mode Networks - Cheng Wang, Carter T. Butts, John Hipp, Cynthia M. Lakon, 2020</title>
    <dc:date>2020-12-16T21:32:30+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/0049124120914933</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to reproduce network structure over time, to date there are few indices for assessing the ability of the model to reproduce individuals’ behavior patterns. Drawing on the widely used strategy of assessing model adequacy by comparing index values summarizing features of the observed data to the distribution of those index values on simulated data from the fitted model, we propose four goals that a researcher could reasonably expect of a joint structure/behavior model regarding how well it captures behavior and describe indices for assessing each of these. These reasonably simple and easily implemented indices can be used for assessing model adequacy with any dynamic network models jointly working with networks and behavior, including the stochastic actor-based models implemented within software packages such as RSien version 1.2-24. We demonstrate the use of our indices with an empirical example to show how they can be employed in practical settings, with an additional extension to modeling affiliation dynamics in two-mode networks. Key scripts are provided in the Supplemental Document (which can be found at http://smr.sagepub.com/supplemental/)."]]></description>
<dc:subject>network_data_analysis networks_in_and_over_time exponential_family_random_graphs goodness-of-fit statistics to_teach:baby-nets butts.carter_t. re:phil-of-bayes_paper simulation-based_inference model_checking in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0c6f1d71eae6/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:butts.carter_t."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation-based_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
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<item rdf:about="http://philsci-archive.pitt.edu/18496/">
    <title>Collectivist Foundations for Bayesian Statistics - PhilSci-Archive</title>
    <dc:date>2020-12-12T16:04:04+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/18496/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What (if anything) justifies the use of Bayesian statistics in science? The traditional answer is that Bayesian statistics is simply an instance of orthodox expected utility theory. Thus, Bayesian statistical methods, like principles of utility theory, are justified by norms of individual rationality. In particular, most Bayesians argue that a scientist's credences must satisfy the probability axioms if she adheres to norms of practical and epistemic (individual) rationality. We argue that, to justify Bayesian statistics as a tool for science, it is necessary that a scientist's public credences (i.e., her degrees of belief qua scientist) obey the probability axioms. We claim that norms of collective science help justify this restricted view, termed public probabilism."]]></description>
<dc:subject>to:NB bayesianism philosophy_of_science science_as_a_social_process mayo-wilson.conor re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:09130a3e5710/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mayo-wilson.conor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
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<item rdf:about="https://arxiv.org/abs/1909.06523">
    <title>[1909.06523] Justifying the Norms of Inductive Inference</title>
    <dc:date>2019-09-18T12:55:38+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.06523</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favoring, even when that is inappropriate. The purpose of this paper is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favoring is not necessarily the likelihood. I use an accuracy argument to argue for probabilism and I develop a new kind of argument to argue for two general updating rules, both of which are reasonable in different contexts. One of the updating rules has standard Bayesian updating, Bissiri et al's (2016) general Bayesian updating, Douven's (2016) IBE-based updating, and Vassend's (2019a) quasi-Bayesian updating as special cases. The other updating rule is novel."]]></description>
<dc:subject>to:NB bayesianism philosophy_of_science re:phil-of-bayes_paper color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a5839291fd59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
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</item>
<item rdf:about="https://amstat.tandfonline.com/doi/full/10.1080/10618600.2019.1637749">
    <title>Testing Sparsity-Inducing Penalties: Journal of Computational and Graphical Statistics: Vol 0, No 0</title>
    <dc:date>2019-08-20T16:07:38+00:00</dc:date>
    <link>https://amstat.tandfonline.com/doi/full/10.1080/10618600.2019.1637749</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many penalized maximum likelihood estimators correspond to posterior mode estimators under specific prior distributions. Appropriateness of a particular class of penalty functions can therefore be interpreted as the appropriateness of a prior for the parameters. For example, the appropriateness of a lasso penalty for regression coefficients depends on the extent to which the empirical distribution of the regression coefficients resembles a Laplace distribution. We give a testing procedure of whether or not a Laplace prior is appropriate and accordingly, whether or not using a lasso penalized estimate is appropriate. This testing procedure is designed to have power against exponential power priors which correspond to ℓqℓq penalties. Via simulations, we show that this testing procedure achieves the desired level and has enough power to detect violations of the Laplace assumption when the numbers of observations and unknown regression coefficients are large. We then introduce an adaptive procedure that chooses a more appropriate prior and corresponding penalty from the class of exponential power priors when the null hypothesis is rejected. We show that this can improve estimation of the regression coefficients both when they are drawn from an exponential power distribution and when they are drawn from a spike-and-slab distribution. Supplementary materials for this article are available online."

--- I feel like I fundamentally disagree with this approach.  Those priors are merely (to quote Jamie Robins and Larry Wasserman) "frequentist pursuit", and have no bearing on whether (say) the Lasso will give a good sparse, linear approximation to the underlying regression function (see https://normaldeviate.wordpress.com/2013/09/11/consistency-sparsistency-and-presistency/).  All of which said, Hoff is always worth listening to, so the last tag applies with special force.]]></description>
<dc:subject>to:NB model_checking sparsity regression hypothesis_testing bayesianism re:phil-of-bayes_paper hoff.peter to_besh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aaba8d8a838f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sparsity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hoff.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_besh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.00882">
    <title>[1908.00882] Population Predictive Checks</title>
    <dc:date>2019-08-05T12:47:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.00882</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian modeling has become a staple for researchers analyzing data. Thanks to recent developments in approximate posterior inference, modern researchers can easily build, use, and revise complicated Bayesian models for large and rich data. These new abilities, however, bring into focus the problem of model assessment. Researchers need tools to diagnose the fitness of their models, to understand where a model falls short, and to guide its revision. In this paper we develop a new method for Bayesian model checking, the population predictive check (Pop-PC). Pop-PCs are built on posterior predictive checks (PPC), a seminal method that checks a model by assessing the posterior predictive distribution on the observed data. Though powerful, PPCs use the data twice---both to calculate the posterior predictive and to evaluate it---which can lead to overconfident assessments. Pop-PCs, in contrast, compare the posterior predictive distribution to the population distribution of the data. This strategy blends Bayesian modeling with frequentist assessment, leading to a robust check that validates the model on its generalization. Of course the population distribution is not usually available; thus we use tools like the bootstrap and cross validation to estimate the Pop-PC. Further, we extend Pop-PCs to hierarchical models. We study Pop-PCs on classical regression and a hierarchical model of text. We show that Pop-PCs are robust to overfitting and can be easily deployed on a broad family of models."
]]></description>
<dc:subject>to:NB model_checking bayesianism statistics blei.david re:phil-of-bayes_paper to_read cross-validation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:221ff74c92f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blei.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cross-validation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12393?af=R">
    <title>Alternatives to post‐processing posterior predictive p values - Gåsemyr - - Scandinavian Journal of Statistics - Wiley Online Library</title>
    <dc:date>2019-06-15T16:54:04+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12393?af=R</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The posterior predictive p value (ppp) was invented as a Bayesian counterpart to classical p values. The methodology can be applied to discrepancy measures involving both data and parameters and can, hence, be targeted to check for various modeling assumptions. The interpretation can, however, be difficult since the distribution of the ppp value under modeling assumptions varies substantially between cases. A calibration procedure has been suggested, treating the ppp value as a test statistic in a prior predictive test. In this paper, we suggest that a prior predictive test may instead be based on the expected posterior discrepancy, which is somewhat simpler, both conceptually and computationally. Since both these methods require the simulation of a large posterior parameter sample for each of an equally large prior predictive data sample, we furthermore suggest to look for ways to match the given discrepancy by a computation‐saving conflict measure. This approach is also based on simulations but only requires sampling from two different distributions representing two contrasting information sources about a model parameter. The conflict measure methodology is also more flexible in that it handles non‐informative priors without difficulty. We compare the different approaches theoretically in some simple models and in a more complex applied example."]]></description>
<dc:subject>to:NB bayesianism statistics model_checking re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0616078fac6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nostalgebraist.tumblr.com/post/161645122124/bayes-a-kinda-sorta-masterpost">
    <title>trees are harlequins, words are harlequins — bayes: a kinda-sorta masterpost</title>
    <dc:date>2017-08-13T16:06:12+00:00</dc:date>
    <link>http://nostalgebraist.tumblr.com/post/161645122124/bayes-a-kinda-sorta-masterpost</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[(Last tag is for the cultists whom the poster is [more or less explicitly] going after)]]></description>
<dc:subject>bayesianism statistics foundations_of_statistics utter_stupidity re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:497d84bc939b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:utter_stupidity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1511.01844">
    <title>[1511.01844] A note on the evaluation of generative models</title>
    <dc:date>2016-12-20T18:51:40+00:00</dc:date>
    <link>https://arxiv.org/abs/1511.01844</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided."]]></description>
<dc:subject>to:NB simulation stochastic_models model_checking statistics via:vaguery to_read re:ADAfaEPoV re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6d4902c7cfcf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:vaguery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/113/34/9569.abstract.html">
    <title>Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures</title>
    <dc:date>2016-08-24T16:14:25+00:00</dc:date>
    <link>http://www.pnas.org/content/113/34/9569.abstract.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM."]]></description>
<dc:subject>to:NB phylogenetics statistics re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:09df42e1a901/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phylogenetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ndpr.nd.edu/news/59657-reconstructing-reality-models-mathematics-and-simulations/">
    <title>Reconstructing Reality: Models, Mathematics, and Simulations // Reviews // Notre Dame Philosophical Reviews // University of Notre Dame</title>
    <dc:date>2016-01-17T14:09:41+00:00</dc:date>
    <link>https://ndpr.nd.edu/news/59657-reconstructing-reality-models-mathematics-and-simulations/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>in_NB books:noted philosophy_of_science simulation modeling re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:455c316ef510/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.3442">
    <title>[1412.3442] Posterior predictive p-values and the convex order</title>
    <dc:date>2015-01-20T13:23:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.3442</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Posterior predictive p-values are a common approach to Bayesian model-checking. This article analyses their frequency behaviour, that is, their distribution when the parameters and the data are drawn from the prior and the model respectively. We show that the family of possible distributions is exactly described as the distributions that are less variable than uniform on [0,1], in the convex order. In general, p-values with such a property are not conservative, and we illustrate how the theoretical worst-case error rate for false rejection can occur in practice. We describe how to correct the p-values to recover conservatism in several common scenarios, for example, when interpreting a single p-value or when combining multiple p-values into an overall score of significance. We also handle the case where the p-value is estimated from posterior samples obtained from techniques such as Markov Chain or Sequential Monte Carlo. Our results place posterior predictive p-values in a much clearer theoretical framework, allowing them to be used with more assurance."]]></description>
<dc:subject>to:NB to_read model_checking bayesianism re:phil-of-bayes_paper hypothesis_testing p-values statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:de3ce3593c11/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:p-values"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1407.0050">
    <title>[1407.0050] Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure</title>
    <dc:date>2015-01-20T02:36:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1407.0050</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Admixture models are a ubiquitous approach to capture latent population structure in genetic samples. Despite the widespread application of admixture models, little thought has been devoted to the quality of the model fit or the accuracy of the estimates of parameters of interest for a particular study. Here we develop methods for validating admixture models based on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of a statistical model. We develop PPCs for five population-level statistics of interest: within-population genetic variation, background linkage disequilibrium, number of ancestral populations, between-population genetic variation, and the downstream use of admixture parameters to correct for population structure in association studies. Using PPCs, we evaluate the quality of the model estimates for four qualitatively different population genetic data sets: the POPRES European individuals, the HapMap phase 3 individuals, continental Indians, and African American individuals. We found that the same model fitted to different genomic studies resulted in highly study-specific results when evaluated using PPCs, illustrating the utility of PPCs for model-based analyses in large genomic studies."]]></description>
<dc:subject>to:NB genetics statistics re:phil-of-bayes_paper blei.david</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:586b1c618d0b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blei.david"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/111/33/11973.full">
    <title>Testing for ontological errors in probabilistic forecasting models of natural systems</title>
    <dc:date>2014-08-20T15:52:37+00:00</dc:date>
    <link>http://www.pnas.org/content/111/33/11973.full</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not."

--- Contributed rather than peer-reviewed, so who knows?]]></description>
<dc:subject>to:NB to_read statistics model_checking re:phil-of-bayes_paper foundations_of_statistics prediction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ae2487840f68/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0302">
    <title>[1312.0302] On the Equivalence between Bayesian and Classical Hypothesis Testing</title>
    <dc:date>2013-12-16T15:04:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0302</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For hypotheses of the type H_0:theta=theta_0 vs H_1:theta ne theta_0 we demonstrate the equivalence of a Bayesian hypothesis test using a Bayes factor and the corresponding classical test, for a large class of models, which are detailed in the paper. In particular, we show that the role of the prior and critical region for the Bayes factor test is only to specify the type I error. This is their only role since, as we show, the power function of the Bayes factor test coincides exactly with that of the classical test, once the type I error has been fixed. 
"For more complex tests involving nuisance parameters, we recover the classical test by using Jeffreys prior on the nuisance parameters, while the prior on the hypothesized parameters can be arbitrary up to a large class. On the other hand, we show that using proper priors on the nuisance parameters results in a test with uniformly lower power than the classical test."

- Ouch.]]></description>
<dc:subject>have_read hypothesis_testing bayesianism statistics re:phil-of-bayes_paper in_NB to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1a00bd4bbb59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dx.doi.org/10.1214/13-BA024">
    <title>Bayesian Estimation of the Discrepancy with Misspecified Parametric Models</title>
    <dc:date>2013-07-11T23:04:48+00:00</dc:date>
    <link>http://dx.doi.org/10.1214/13-BA024</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study a Bayesian model where we have made specific requests about the parameter values to be estimated. The aim is to find the parameter of a parametric family which minimizes a distance to the data generating density and then to estimate the discrepancy using nonparametric methods. We illustrate how coherent updating can proceed given that the standard Bayesian posterior from an unidentifiable model is inappropriate. Our updating is performed using Markov Chain Monte Carlo methods and in particular a novel method for dealing with intractable normalizing constants is required. Illustrations using synthetic data are provided."]]></description>
<dc:subject>statistics estimation bayesian_consistency misspecification re:phil-of-bayes_paper in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6488b86f8b46/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesian_consistency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://users.soe.ucsc.edu/~draper/draper-krnjajic-draft-2011.pdf">
    <title>Calibration results for Bayesian model specification</title>
    <dc:date>2013-04-10T16:26:49+00:00</dc:date>
    <link>http://users.soe.ucsc.edu/~draper/draper-krnjajic-draft-2011.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When the goal is inference about an unknown θ and prediction of future data D∗ on the basis of data D and background assumptions/judgments B, the process of Bayesian model specification involves two ingredients: the condi- tional probability distributions p(θ|B) and p(D|θ, B). Here we focus on specifying p(D|θ,B), and we argue that calibration considerations — paying attention to how often You get the right answer — should be an integral part of this specifi- cation process. After contrasting Bayes-factor-based and predictive model-choice criteria, we present some calibration results, in fixed- and random-effects Poisson models, relevant to addressing two of the basic questions that arise in Bayesian model specification: (Q1) Is model Mj better than Mj′ ? and (Q2) Is model Mj∗ good enough? In particular, we show that LSF S , a full-sample log score predictive model-choice criterion, has better small-sample model discrimination performance than either DIC or a cross-validation-style log-scoring criterion, in the simulation setting we consider; we examine the large-sample behavior of LSFS; and we (a) demonstrate that the popular posterior predictive tail-area method for answering a question related to Q2 can be poorly calibrated and (b) document the success of a method for calibrating it."]]></description>
<dc:subject>to:NB to_read model_selection bayesianism calibration model_checking hypothesis_testing re:phil-of-bayes_paper misspecification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:48b44f050ab7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:calibration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ssu/1356628931">
    <title>Vehtari , Ojanen : A survey of Bayesian predictive methods for model assessment, selection and comparison</title>
    <dc:date>2012-12-27T19:33:47+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ssu/1356628931</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To date, several methods exist in the statistical literature for model assessment, which purport themselves specifically as Bayesian predictive methods. The decision theoretic assumptions on which these methods are based are not always clearly stated in the original articles, however. The aim of this survey is to provide a unified review of Bayesian predictive model assessment and selection methods, and of methods closely related to them. We review the various assumptions that are made in this context and discuss the connections between different approaches, with an emphasis on how each method approximates the expected utility of using a Bayesian model for the purpose of predicting future data."]]></description>
<dc:subject>bayesianism model_checking prediction statistics re:phil-of-bayes_paper in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:80ca07e28a1c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.cwi.nl/~pdg/ftp/alt12longer.pdf">
    <title>The Safe Bayesian: learning the learning rate via the mixability gap</title>
    <dc:date>2012-07-13T01:18:25+00:00</dc:date>
    <link>http://homepages.cwi.nl/~pdg/ftp/alt12longer.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Standard Bayesian inference can behave suboptimally if the model is wrong. We present a modification of Bayesian inference which continues to achieve good rates with wrong models. Our method adapts the Bayesian learning rate to the data, picking the rate minimizing the cumulative loss of sequential prediction by posterior randomization. Our results can also be used to adapt the learning rate in a PAC-Bayesian context. The results are based on an extension of an inequality due to T. Zhang and others to dependent random variables."]]></description>
<dc:subject>heard_the_talk bayesianism learning_theory statistics misspecification re:phil-of-bayes_paper grunwald.peter in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:311c9df3d129/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:grunwald.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/pss/10.1086/662283">
    <title>Projective Evidence and the Heterogeneity of Scientific Confirmation - JSTOR: Philosophy of Science, Vol. 78, No. 5 (December &lt;span class=&quot;smallcaps&quot;&gt;2011&lt;/span&gt;), pp. 887-899</title>
    <dc:date>2012-01-08T14:19:04+00:00</dc:date>
    <link>http://www.jstor.org/pss/10.1086/662283</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I contrast our own evidence for the hypothesis of organic fossil origins with that available in previous centuries, suggesting that the most powerful contemporary evidence consists in a form of projective support whose distinctive features are not well captured by familiar hypothetico-deductive, abductive, or even more recent and more technically sophisticated (e.g., Bayesian) accounts of scientific confirmation. I suggest that such accounts either misrepresent or ignore something important about the heterogeneous ways in which scientific hypotheses can be supported by evidence, and I go on to suggest that the search for any single such account may be misguided in any case."]]></description>
<dc:subject>to:NB philosophy_of_science paleontology re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3bab84ab52ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:paleontology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.wordsmatter.caltech.edu/SSPapers/sswp1320.pdf">
    <title>Modeling the Change of Paradigm: Non-Bayesian Reactions to Unexpected News (Ortoleva)</title>
    <dc:date>2012-01-05T02:30:20+00:00</dc:date>
    <link>http://www.wordsmatter.caltech.edu/SSPapers/sswp1320.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite its normative appeal and widespread use, Bayes’ rule has two well-known limitations: first, it does not predict how agents should react to an information to which they assigned probability zero; second, a sizable empirical evidence documents how agents systematically deviate from its prescriptions by overreacting to information to which they assigned a positive but small probability. By replacing Dynamic Consistency with a novel axiom, Dynamic Coherence, we characterize an alternative updating rule that is not subject to these limitations, but at the same time coincides with Bayes’ rule for “normal” events. In particular, we model an agent with a utility function over consequences, a prior over priors ρ, and a threshold. In the first period she chooses the prior that maximizes the prior over priors ρ - a’ la maximum likelihood. As new information is revealed: if the chosen prior assigns to this information a probability above the threshold, she follows Bayes’ rule and updates it. Otherwise, she goes back to her prior over priors ρ, updates it using Bayes’ rule, and then chooses the new prior that maximizes the updated ρ. We also extend our analysis to the case of ambiguity aversion."]]></description>
<dc:subject>to:NB to_read decision_theory bayesianism statistics re:phil-of-bayes_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:edb233896c20/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rochester.edu/college/psc/clarke/POPArticle.pdf">
    <title>Modernizing Political Science: A Model-Based Approach</title>
    <dc:date>2011-10-18T19:52:04+00:00</dc:date>
    <link>http://rochester.edu/college/psc/clarke/POPArticle.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB to_read political_science philosophy_of_science modeling re:phil-of-bayes_paper clarke.kevin primo.david</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:43873bec44a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clarke.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:primo.david"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio?isbn=978-0195382204">
    <title>A Model Discipline: Political Science and the Logic of Representations by - Powell's Books</title>
    <dc:date>2011-10-18T17:53:23+00:00</dc:date>
    <link>http://www.powells.com/biblio?isbn=978-0195382204</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted philosophy_of_science political_science re:phil-of-bayes_paper clarke.kevin primo.david via:scotte modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d20641798167/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clarke.kevin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:primo.david"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:scotte"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://delong.typepad.com/sdj/2011/10/calibration-and-econometric-non-practice.html?">
    <title>Calibration and Econometric Non-Practice</title>
    <dc:date>2011-10-17T23:07:38+00:00</dc:date>
    <link>http://delong.typepad.com/sdj/2011/10/calibration-and-econometric-non-practice.html?</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[DeLong is missing a trick.  The rational-expectations dogmatist could simply insist that the true probability of an event like 2008 in 2008 _was_ 0.02%, and we were just unlucky.]]></description>
<dc:subject>macroeconomics econometrics rational_expectations calibration re:phil-of-bayes_paper statistics delong.brad model_checking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f0bb44d9be55/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rational_expectations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:calibration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:delong.brad"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mpra.ub.uni-muenchen.de/34117/">
    <title>From Wald to Savage: homo economicus becomes a Bayesian statistician - Munich Personal RePEc Archive</title>
    <dc:date>2011-10-17T18:56:32+00:00</dc:date>
    <link>http://mpra.ub.uni-muenchen.de/34117/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian rationality is the paradigm of rational behavior in neoclassical economics. A rational agent in an economic model is one who maximizes her subjective expected utility and consistently revises her beliefs according to Bayes’s rule. The paper raises the question of how, when and why this characterization of rationality came to be endorsed by mainstream economists. Though no definitive answer is provided, it is argued that the question is far from trivial and of great historiographic importance. The story begins with Abraham Wald’s behaviorist approach to statistics and culminates with Leonard J. Savage’s elaboration of subjective expected utility theory in his 1954 classic The Foundations of Statistics. It is the latter’s acknowledged fiasco to achieve its planned goal, the reinterpretation of traditional inferential techniques along subjectivist and behaviorist lines, which raises the puzzle of how a failed project in statistics could turn into such a tremendous hit in economics. A couple of tentative answers are also offered, involving the role of the consistency requirement in neoclassical analysis and the impact of the postwar transformation of US business schools."  --- The guess about business schools at the end seems plausible.]]></description>
<dc:subject>re:phil-of-bayes_paper bayesianism statistics decision_theory economics history_of_statistics history_of_economics have_read wald.abraham savage.leonard_j. foundations_of_statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a7fe66fd71e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wald.abraham"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:savage.leonard_j."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1312204002">
    <title>Little : Calibrated Bayes, for Statistics in General, and Missing Data in Particular</title>
    <dc:date>2011-08-01T16:45:47+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1312204002</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["the Calibrated Bayesian (CB) approach to statistical inference capitalizes on the strength of Bayesian and frequentist approaches to statistical inference. In the CB approach, inferences under a particular model are Bayesian, but frequentist methods are useful for model development and model checking. In this article the CB approach is outlined. Bayesian methods for missing data are then reviewed from a CB perspective. The basic theory of the Bayesian approach, and the closely related technique of multiple imputation, is described. Then applications of the Bayesian approach to normal models are described, both for monotone and nonmonotone missing data patterns. Sequential Regression Multivariate Imputation and Penalized Spline of Propensity Models are presented as two useful approaches for relaxing distributional assumptions."  Also http://arxiv.org/abs/1108.1917
]]></description>
<dc:subject>statistics bayesianism re:phil-of-bayes_paper to:NB to_read model_checking</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:51e2f2367743/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/v6g768437203h250/">
    <title>Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models</title>
    <dc:date>2011-07-13T22:06:47+00:00</dc:date>
    <link>http://www.springerlink.com/content/v6g768437203h250/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the posterior expected utility. Experimental confirmation of the models, however, has been claimed because of groups of agents that “probability match” the posterior. Probability matching only constitutes support for the Bayesian claim if additional unobvious and untested (but testable) assumptions are invoked. The alternative strategy of weakening the underlying notion of rationality no longer distinguishes the Bayesian model uniquely."
]]></description>
<dc:subject>philosophy_of_science cognitive_science bayesianism kith_and_kin have_read re:phil-of-bayes_paper blogged eberhardt.frederick danks.david</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f9f68f6ba557/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogged"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:eberhardt.frederick"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:danks.david"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1106.2895">
    <title>[1106.2895] Statistical Inference: The Big Picture</title>
    <dc:date>2011-06-16T04:17:57+00:00</dc:date>
    <link>http://arxiv.org/abs/1106.2895</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>re:phil-of-bayes_paper heard_the_talk kith_and_kin have_read to:blog kass.robert</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:66e47b62d8cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kass.robert"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/8616/">
    <title>Irrelevant Conjunction and the Ratio Measure or Historical Skepticism - PhilSci-Archive</title>
    <dc:date>2011-05-26T19:53:35+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/8616/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is widely believed that one should not become more confident that _all swans are white and all lions are brave_ simply by observing white swans. Irrelevant conjunction or "tacking" of a theory onto another is often thought problematic for Bayesianism, especially given the ratio measure of confirmation considered here... Using the ratio measure, the irrelevant conjunction is confirmed to the same degree as the relevant conjunct, which... is ideal: the irrelevant conjunct is irrelevant. Because the past's really having been as it now appears to have been is an irrelevant conjunct, present evidence confirms theories about past events only insofar as irrelevant conjunctions are confirmed. Hence the ideal of not confirming irrelevant conjunctions would imply that historical claims are not confirmed. ..."
]]></description>
<dc:subject>philosophy_of_science bayesianism boltzmann_brains to:NB re:phil-of-bayes_paper</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dfdc21e98ee6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:boltzmann_brains"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/0426151n302h8804/">
    <title>A frequentist interpretation of probability for model-based inductive inference</title>
    <dc:date>2011-03-03T12:35:10+00:00</dc:date>
    <link>http://www.springerlink.com/content/0426151n302h8804/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>probability induction philosophy_of_science foundations_of_statistics re:phil-of-bayes_paper spanos.aris</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:77eb22ca570b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spanos.aris"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.people.fas.harvard.edu/~pgs/InductionSamplesKinds_INPC_final.pdf">
    <title>&quot;Induction, Samples, and Kinds&quot; (Godfrey-Smith)</title>
    <dc:date>2010-11-08T01:23:45+00:00</dc:date>
    <link>http://www.people.fas.harvard.edu/~pgs/InductionSamplesKinds_INPC_final.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>induction philosophy_of_science have_read re:phil-of-bayes_paper</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e32781764093/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.people.fas.harvard.edu/~pgs/PGS-StrategyMBS-06.pdf">
    <title>The strategy of model-based science (Godfrey-Smith, 2006)</title>
    <dc:date>2010-11-08T01:20:32+00:00</dc:date>
    <link>http://www.people.fas.harvard.edu/~pgs/PGS-StrategyMBS-06.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to_read modeling philosophy_of_science re:phil-of-bayes_paper godfrey-smith.peter</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:196ee032c04f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:godfrey-smith.peter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.people.fas.harvard.edu/~pgs/PGSonPopper.pdf">
    <title>&quot;Popper's Philosophy of Science: Looking Ahead&quot; (Godfrey-Smith)</title>
    <dc:date>2010-11-08T01:16:16+00:00</dc:date>
    <link>http://www.people.fas.harvard.edu/~pgs/PGSonPopper.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I like the parts about how it's more important to have a sound epistemology about _revising_ our beliefs (i.e., changing our minds), than about warranting our beliefs at any one time.
]]></description>
<dc:subject>have_read philosophy_of_science re:phil-of-bayes_paper godfrey-smith.peter popper.karl_r.</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10552c27943a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:godfrey-smith.peter"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:popper.karl_r."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.journals.uchicago.edu/doi/abs/10.1086/656009">
    <title>&quot;Is Frequentist Testing Vulenrable to the Base-Rate Fallacy?&quot; (Spanos) - Philosophy of Science</title>
    <dc:date>2010-10-03T18:48:39+00:00</dc:date>
    <link>http://www.journals.uchicago.edu/doi/abs/10.1086/656009</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article calls into question the charge that frequentist testing is susceptible to the base-rate fallacy. It is argued that the apparent similarity between examples like the Harvard Medical School test and frequentist testing is highly misleading. A closer scrutiny reveals that such examples have none of the basic features of a proper frequentist test, such as legitimate data, hypotheses, test statistics, and sampling distributions. Indeed, the relevant error probabilities are replaced with the false positive/negative rates that constitute deductive calculations based on known probabilities among events. As a result, the ampliative dimension of frequentist induction—learning from data about the underlying data-generating mechanism—is missing."
]]></description>
<dc:subject>statistics philosophy_of_science re:phil-of-bayes_paper hypothesis_testing spanos.aris</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5f0398164115/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spanos.aris"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.3868">
    <title>[1006.3868] Philosophy and the practice of Bayesian statistics</title>
    <dc:date>2010-06-22T00:10:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.3868</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Bwahahahaha!
]]></description>
<dc:subject>bayesianism data_analysis foundations_of_statistics self-centered re:phil-of-bayes_paper philosophy_of_science gelman.andrew model_checking</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9b880937c4c1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-centered"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gelman.andrew"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1005.5483">
    <title>[1005.5483] Model Selection Principles in Misspecified Models</title>
    <dc:date>2010-06-01T14:25:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.5483</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[So-so.  Suspect that most of these results are actually in Claeskens and Hjort's book, but am insufficiently motivated to check.
]]></description>
<dc:subject>model_selection misspecification statistics re:phil-of-bayes_paper have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:32be44d855fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pitt.edu/~jdnorton/papers/material.pdf">
    <title>A Material Theory of Induction</title>
    <dc:date>2010-05-24T21:03:00+00:00</dc:date>
    <link>http://www.pitt.edu/~jdnorton/papers/material.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume’s problem of the justification of induction."
]]></description>
<dc:subject>induction epistemology philosophy_of_science have_read re:phil-of-bayes_paper norton.john_d.</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2137b675bd7d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:norton.john_d."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://philsci-archive.pitt.edu/archive/00001446/">
    <title>PhilSci Archive - A Little Survey of Induction</title>
    <dc:date>2010-05-24T03:36:27+00:00</dc:date>
    <link>http://philsci-archive.pitt.edu/archive/00001446/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I like this.
]]></description>
<dc:subject>induction philosophy_of_science philosophy re:phil-of-bayes_paper have_read norton.john_d.</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c32b75a23e1e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:induction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:norton.john_d."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://psycnet.apa.org/journals/rev/107/2/358/">
    <title>How persuasive is a good fit? A comment on theory testing.</title>
    <dc:date>2010-04-21T14:40:27+00:00</dc:date>
    <link>http://psycnet.apa.org/journals/rev/107/2/358/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Everything useful in this paper is contained in their Figure 1 and its caption, and even then I think they're incomplete.  (In the top left of Figure 1, the "strong support" quadrant, draw another narrow band along the opposite diagonal to the first theory, also going through the small cross of observations: this would be a distinct and incompatible theory which also makes a narrow range of predictions that also match the precisely-measured data.)
]]></description>
<dc:subject>methodological_advice hypothesis_testing statistics psychology via:kass have_read re:phil-of-bayes_paper</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab4ac947389d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:methodological_advice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1270041257">
    <title>Lindsay, Liu: Model Assessment Tools for a Model False World</title>
    <dc:date>2010-04-01T17:37:02+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1270041257</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["a model credibility index, which is designed to serve as a one-number summary measure of model adequacy. We define the index to be the maximum sample size at which samples from the model and those from the true data generating mechanism are nearly indistinguishable. We use standard notions from hypothesis testing to make this definition precise. We use data subsampling to estimate the index" --- To be blogged, after the paper with Andy is done.
]]></description>
<dc:subject>statistics misspecification re:phil-of-bayes_paper hypothesis_testing bootstrap have_read to:blog</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c4be6608d034/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/9218.html">
    <title>Geweke, J.: Complete and Incomplete Econometric Models.</title>
    <dc:date>2010-01-31T18:27:42+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9218.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I will be fascinated to see what of this is "Bayesian".
]]></description>
<dc:subject>books:noted re:phil-of-bayes_paper re:your_favorite_dsge_sucks econometrics simulation statistics misspecification bayesianism books:owned</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30f2cad66528/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1001.4656">
    <title>[1001.4656] On Bayesian Data Analysis</title>
    <dc:date>2010-01-29T14:57:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1001.4656</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>bayesianism statistics re:phil-of-bayes_paper</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1cd8d86499a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www3.interscience.wiley.com/journal/123266863/abstract">
    <title>Likelihood for statistically equivalent models. John Copas. 2010; JRSS B</title>
    <dc:date>2010-01-29T14:26:16+00:00</dc:date>
    <link>http://www3.interscience.wiley.com/journal/123266863/abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In likelihood inference we usually assume that the model is fixed and then base inference on the corresponding likelihood function. Often, however, the choice of model is rather arbitrary, and there may be other models which fit the data equally well. We study robustness of likelihood inference over such 'statistically equivalent' models and suggest a simple 'envelope likelihood' to capture this aspect of model uncertainty. Robustness depends critically on how we specify the parameter of interest. Some asymptotic theory is presented, illustrated by three examples."
]]></description>
<dc:subject>statistics estimation likelihood model_uncertainty misspecification re:phil-of-bayes_paper to_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:58fcc69c6f1b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:likelihood"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ssu/1239113309">
    <title>Commenges: Statistical models: Conventional, penalized and hierarchical likelihood</title>
    <dc:date>2009-12-31T18:48:47+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ssu/1239113309</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We give an overview of statistical models and likelihood, together with two of its variants: penalized and hierarchical likelihood. The Kullback-Leibler divergence is referred to repeatedly in the literature, for defining the misspecification risk of a model and for grounding the likelihood and the likelihood cross-validation, which can be used for choosing weights in penalized likelihood. Families of penalized likelihood and particular sieves estimators are shown to be equivalent. The similarity of these likelihoods with a posteriori distributions in a Bayesian approach is considered."
]]></description>
<dc:subject>statistics likelihood cross-validation re:phil-of-bayes_paper to_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:04fa6503ab91/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:likelihood"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cross-validation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1262271622">
    <title>Evans, Jang: Invariant P-values for model checking</title>
    <dc:date>2009-12-31T16:49:05+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1262271622</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Interesting, but I suspect the bits about approximating an underlying discrete distribution could be lifted...
]]></description>
<dc:subject>statistics hypothesis_testing p-values re:phil-of-bayes_paper have_read model_checking</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ac6bd628cb55/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:p-values"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0912.4269">
    <title>[0912.4269] Martingales and p-values as measures of evidence</title>
    <dc:date>2009-12-28T16:39:28+00:00</dc:date>
    <link>http://arxiv.org/abs/0912.4269</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Weird.  Wonder what Mayo would make of this.
]]></description>
<dc:subject>martingales statistics p-values hypothesis_testing re:phil-of-bayes_paper to:NB have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:02dd2e7604ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:martingales"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:p-values"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/pss/193077">
    <title>The Appraisal of Theories: Kuhn Meets Bayes (Salmon, 1990)</title>
    <dc:date>2009-11-21T13:44:53+00:00</dc:date>
    <link>http://www.jstor.org/pss/193077</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[A surprisingly weak paper, along the lines of "hey! did you realize that you can use the prior distribution to penalize things other than not fitting the data?", but I should re-read.  Plus: this only makes sense if everyone always had both the old and the new paradigms in the support of their priors. ("Surprising", because Salmon was very good.)
]]></description>
<dc:subject>philosophy_of_science bayesianism have_read re:phil-of-bayes_paper salmon.wesley_c.</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2723524f9ab5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:salmon.wesley_c."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1199285031">
    <title>Bayesian Checking of the Second Levels of Hierarchical Models</title>
    <dc:date>2009-07-30T14:47:27+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1199285031</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[In particular see the bit about "pure Bayesian reasoning" in the rejoinder.
]]></description>
<dc:subject>statistics modeling bayesianism re:phil-of-bayes_paper have_read model_checking hierarchical_statistical_models</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da48e8485ddb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hierarchical_statistical_models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.jstor.org/stable/2982519">
    <title>Inference and Stochastic Processes (Bartlett)</title>
    <dc:date>2009-06-19T15:35:00+00:00</dc:date>
    <link>http://www.jstor.org/stable/2982519</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I hope [my philosophy of statistics is] sufficiently undogmatic not to imply that all those who may think rather differently from me are necessarily stupid.  If at times I do seem dogmatic, it is because it is convenient to give my own views as unequivocally as possible."
]]></description>
<dc:subject>statistical_inference_for_stochastic_processes foundations_of_statistics bayesianism re:phil-of-bayes_paper bartlett.m.s. have_read in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:677a9d51a506/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bartlett.m.s."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kellogg.northwestern.edu/faculty/weinstein/htm/learnability0208.pdf">
    <title>Testing Theories with Learnable and Predictive Representations</title>
    <dc:date>2009-03-20T15:55:18+00:00</dc:date>
    <link>http://www.kellogg.northwestern.edu/faculty/weinstein/htm/learnability0208.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the problem of testing an expert whose theory has a learnable and predictive parametric representation, as do all standard processes used in Bayesian statistics. We design a test in which the expert is required to submit a date T by which he will have learned enough  to deliver sharp predictions about future frequencies. His forecasts are then tested according to a simple hypothesis test. We show that this  test passes an expert who knows the data-generating process and cannot be manipulated by an uninformed one. Such a test is not possible if the theory is unrestricted. "
]]></description>
<dc:subject>statistics learning_theory prediction decision_theory to_read re:phil-of-bayes_paper</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dea026e00482/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>