<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/2501.16839"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2501.15896"/>
	<rdf:li rdf:resource="https://stat.ethz.ch/~maathuis/papers/Handbook.pdf"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s11023-018-9460-y"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1204.6703"/>
	<rdf:li rdf:resource="http://www.aeaweb.org/articles.php?doi=10.1257/jel.49.4.901"/>
	<rdf:li rdf:resource="http://jmlr.csail.mit.edu/papers/v11/perry10a.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1002.4802"/>
	<rdf:li rdf:resource="http://www.pnas.org/content/106/52/22073.abstract"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0910.2098"/>
	<rdf:li rdf:resource="http://psycnet.apa.org/?fa=main.doiLanding&amp;doi=10.1037/a0016262"/>
	<rdf:li rdf:resource="http://www.springerlink.com/content/a7417j44w5278576/"/>
	<rdf:li rdf:resource="http://www.stat.berkeley.edu/tech-reports/609.abstract"/>
	<rdf:li rdf:resource="http://www.phil.cmu.edu/projects/tetrad/"/>
	<rdf:li rdf:resource="http://www.newton.ac.uk/programmes/SCH/seminars/index.html"/>
	<rdf:li rdf:resource="http://www3.interscience.wiley.com/journal/122210829/abstract"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0905.2592"/>
	<rdf:li rdf:resource="http://www.uwm.edu/~tofias/papers/demento.apsa08.pdf"/>
	<rdf:li rdf:resource="http://www.hiit.fi/~buntine/uai2004.html"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1121347636"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0901.4785"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1009210550"/>
	<rdf:li rdf:resource="http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780521844635"/>
	<rdf:li rdf:resource="http://www.stat.cmu.edu/tr/tr750/tr750.html"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://arxiv.org/abs/2501.16839">
    <title>[2501.16839] Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans</title>
    <dc:date>2025-02-03T00:35:34+00:00</dc:date>
    <link>https://arxiv.org/abs/2501.16839</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Among generative neural models, flow matching techniques stand out for their simple applicability and good scaling properties. Here, velocity fields of curves connecting a simple latent and a target distribution are learned. Then the corresponding ordinary differential equation can be used to sample from a target distribution, starting in samples from the latent one. This paper reviews from a mathematical point of view different techniques to learn the velocity fields of absolutely continuous curves in the Wasserstein geometry. We show how the velocity fields can be characterized and learned via i) transport plans (couplings) between latent and target distributions, ii) Markov kernels and iii) stochastic processes, where the latter two include the coupling approach, but are in general broader. Besides this main goal, we show how flow matching can be used for solving Bayesian inverse problems, where the definition of conditional Wasserstein distances plays a central role. Finally, we briefly address continuous normalizing flows and score matching techniques, which approach the learning of velocity fields of curves from other directions."]]></description>
<dc:subject>to:NB computational_statistics latent_variables stochastic_processes to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b41c5700d66a/</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:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2501.15896">
    <title>[2501.15896] A mirror descent approach to maximum likelihood estimation in latent variable models</title>
    <dc:date>2025-02-03T00:31:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2501.15896</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce an approach based on mirror descent and sequential Monte Carlo (SMC) to perform joint parameter inference and posterior estimation in latent variable models. This approach is based on minimisation of a functional over the parameter space and the space of probability distributions and, contrary to other popular approaches, can be implemented when the latent variable takes values in discrete spaces. We provide a detailed theoretical analysis of both the mirror descent algorithm and its approximation via SMC. We experimentally show that the proposed algorithm outperforms standard expectation maximisation algorithms and is competitive with other popular methods for real-valued latent variables."]]></description>
<dc:subject>to:NB computational_statistics optimization latent_variables em_algorithm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4996f85a32ed/</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:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:em_algorithm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://stat.ethz.ch/~maathuis/papers/Handbook.pdf">
    <title>Handbook of Graphical Models</title>
    <dc:date>2018-07-08T14:54:29+00:00</dc:date>
    <link>https://stat.ethz.ch/~maathuis/papers/Handbook.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This looks terrific.]]></description>
<dc:subject>to:NB graphical_models statistics latent_variables causal_inference causal_discovery maathuis.marloes lauritzen.steffen</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9fafcf409873/</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:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:maathuis.marloes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lauritzen.steffen"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11023-018-9460-y">
    <title>Intervention and Identifiability in Latent Variable Modelling | SpringerLink</title>
    <dc:date>2018-06-23T16:06:00+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11023-018-9460-y</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal models. We then draw a parallel between the latent variable models and Bayesian networks with hidden nodes. This allows us to clarify the use of interventions for dealing with unidentified statistical models. We end by discussing the philosophical and methodological import of our result."]]></description>
<dc:subject>to:NB identifiability causal_inference graphical_models inference_to_latent_objects latent_variables re:g_paper</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:84ff774d65a1/</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:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1204.6703">
    <title>[1204.6703] Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation</title>
    <dc:date>2012-05-01T04:09:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.6703</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Topic models can be seen as a generalization of the clustering problem, in that they posit that observations are generated due to multiple latent factors (e.g. the words in each document are generated as a mixture of several active topics, as opposed to just one). This increased representational power comes at the cost of a more challenging unsupervised learning problem of estimating the topic probability vectors (the distributions over words for each topic), when only the words are observed and the corresponding topics are hidden. 
"We provide a simple and efficient learning procedure that is guaranteed to recover the parameters for a wide class of mixture models, including the popular latent Dirichlet allocation (LDA) model. For LDA, the procedure correctly recovers both the topic probability vectors and the prior over the topics, using only trigram statistics (i.e. third order moments, which may be estimated with documents containing just three words). The method, termed Excess Correlation Analysis (ECA), is based on a spectral decomposition of low order moments (third and fourth order) via two singular value decompositions (SVDs). Moreover, the algorithm is scalable since the SVD operations are carried out on k by k matrices, where k is the number of latent factors (e.g. the number of topics), rather than in the d-dimensional observed space (typically d >> k)."

That's a really remarkable claim, and I'd tag it to_be_shot_after_a_fair_trial if it weren't being made by genuinely serious people.]]></description>
<dc:subject>to_read latent_variables topic_models text_mining mixture_models statistics machine_learning cool_if_true spectral_clustering in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7559d6656342/</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:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:topic_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:text_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixture_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cool_if_true"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spectral_clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jel.49.4.901">
    <title>Nonlinear Models of Measurement Errors</title>
    <dc:date>2011-12-23T21:21:18+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jel.49.4.901</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Measurement errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. While linear errors-in-variables models are usually handled with well-known instrumental variable methods, this article provides an overview of recent research papers that derive estimation methods that provide consistent estimates for nonlinear models with measurement errors. We review models with both classical and nonclassical measurement errors, and with misclassification of discrete variables. For each of the methods surveyed, we describe the key ideas for identification and estimation, and discuss its application whenever it is currently available."  (Not read, reconsider to_teach tag later.)]]></description>
<dc:subject>to:NB statistics latent_variables inference_to_latent_objects instrumental_variables econometrics to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:37bcef54c81e/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v11/perry10a.html">
    <title>A Rotation Test to Verify Latent Structure</title>
    <dc:date>2010-03-03T05:45:42+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v11/perry10a.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>factor_analysis latent_variables in_NB regression owen.art re:g_paper perry.patrick_o.</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:307ee5accc79/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:owen.art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:perry.patrick_o."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1002.4802">
    <title>[1002.4802] Gaussian Process Structural Equation Models with Latent Variables</title>
    <dc:date>2010-02-26T16:15:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1002.4802</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While linear non-Gaussian variants have been well-studied, inference in nonparametric structural equation models is still underdeveloped. We introduce a sparse Gaussian process parameterization that defines a non-linear structure connecting latent variables, unlike common formulations of Gaussian process latent variable models. An efficient Markov chain Monte Carlo procedure is described. We evaluate the stability of the sampling procedure and the predictive ability of the model compared against the current practice."
]]></description>
<dc:subject>statistics graphical_models latent_variables nonparametrics estimation heard_the_talk</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0dc88a078c50/</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:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.pnas.org/content/106/52/22073.abstract">
    <title>Missing and spurious interactions and the reconstruction of complex networks — PNAS</title>
    <dc:date>2009-12-31T22:03:50+00:00</dc:date>
    <link>http://www.pnas.org/content/106/52/22073.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[EM?
]]></description>
<dc:subject>network_data_analysis latent_variables statistics to_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3abbe2a86f00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0910.2098">
    <title>[0910.2098] Overlapping Stochastic Block Models</title>
    <dc:date>2009-10-21T01:58:06+00:00</dc:date>
    <link>http://arxiv.org/abs/0910.2098</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>community_discovery network_data_analysis to_read latent_variables re:stacs</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:15257cdbe4e5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:community_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:stacs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://psycnet.apa.org/?fa=main.doiLanding&amp;doi=10.1037/a0016262">
    <title>A New Lease on Life for Thomson's Bonds Model of Intelligence (Bartholomew, Deary and Lawn, 2009)</title>
    <dc:date>2009-08-01T14:08:48+00:00</dc:date>
    <link>http://psycnet.apa.org/?fa=main.doiLanding&amp;doi=10.1037/a0016262</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I _told_ you so.  (Though they are _shockingly_ naive about fMRI and brain organization.)
]]></description>
<dc:subject>to:blog iq mental_testing factor_analysis psychometrics latent_variables re:g_paper via:moritz-heene i_told_you_so thomson.godfrey spearman.charles</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a3a5a77318be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:iq"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mental_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:moritz-heene"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:i_told_you_so"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:thomson.godfrey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spearman.charles"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.springerlink.com/content/a7417j44w5278576/">
    <title>Particle methods for maximum likelihood estimation in latent variable models</title>
    <dc:date>2009-07-03T23:16:03+00:00</dc:date>
    <link>http://www.springerlink.com/content/a7417j44w5278576/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>latent_variables particle_filters estimation statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b76e209dc5a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:particle_filters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.berkeley.edu/tech-reports/609.abstract">
    <title>Inverse problems as statistics (Evans and Stark, 2001)</title>
    <dc:date>2009-06-28T16:51:20+00:00</dc:date>
    <link>http://www.stat.berkeley.edu/tech-reports/609.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For a statistician, an inverse problem is an inference or estimation problem.  The data are finite in number and contain errors, as they do in classical ... problems, and the unknown typically is infinite-dimensional, as it is in nonparametric regression.  The additional complication in an inverse problem is that the data are only indirectly related to the unknown.  Canonical abstract formulations of statistical estimation problems subsume this complication by allowing probability distributions to be indexed in more-or-less arbitrary ways by parameters, which can be infinite-dimensional.  Standard statistical concepts, questions, and considerations such as bias, variance, mean-squared error, identifiability, consistency, efficiency, and various forms of optimality, apply to inverse problems.  This article discusses inverse problems as statistical estimation and inference problems, and points to the literature for a variety of techniques and results."
]]></description>
<dc:subject>inverse_problems statistics nonparametrics estimation latent_variables to_read to_teach:complexity-and-inference</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f16bd27d4f1d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.phil.cmu.edu/projects/tetrad/">
    <title>Tetrad Project Homepage</title>
    <dc:date>2009-06-28T16:45:24+00:00</dc:date>
    <link>http://www.phil.cmu.edu/projects/tetrad/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Have I really not bookmarked this before?
]]></description>
<dc:subject>tetrad causal_inference graphical_models machine_learning statistics philosophy_of_science latent_variables</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d84e42c6a585/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tetrad"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<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:latent_variables"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.newton.ac.uk/programmes/SCH/seminars/index.html">
    <title>&quot;Statistical Theory and Methods for Complex, High-Dimensional Data&quot;</title>
    <dc:date>2009-06-20T16:21:38+00:00</dc:date>
    <link>http://www.newton.ac.uk/programmes/SCH/seminars/index.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Loads of talks.
]]></description>
<dc:subject>statistics machine_learning model_selection graphical_models regression latent_variables principal_components factor_analysis dimension_reduction lasso bioinformatics track_down_references via:shivak</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5fee06f71a4d/</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:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:principal_components"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dimension_reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lasso"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:shivak"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www3.interscience.wiley.com/journal/122210829/abstract">
    <title>On-line expectation–maximization algorithm for latent data models</title>
    <dc:date>2009-06-02T18:06:51+00:00</dc:date>
    <link>http://www3.interscience.wiley.com/journal/122210829/abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[== http://arxiv.org/abs/0712.4273]]></description>
<dc:subject>statistics latent_variables computational_statistics mixture_models to_teach:data-mining in_NB</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e97c8cd6413a/</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:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixture_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0905.2592">
    <title>[0905.2592] The Sticky HDP-HMM: Bayesian Nonparametric Hidden Markov Models with Persistent States</title>
    <dc:date>2009-05-19T13:37:50+00:00</dc:date>
    <link>http://arxiv.org/abs/0905.2592</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>markov_models change-point_problem statistics latent_variables jordan.michael_i. fox.emily kith_and_kin in_NB heard_the_talk</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9fb2a16c948a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:markov_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:change-point_problem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jordan.michael_i."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fox.emily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.uwm.edu/~tofias/papers/demento.apsa08.pdf">
    <title>Partisan Influence in Congress and Institutional Change</title>
    <dc:date>2009-05-01T16:02:27+00:00</dc:date>
    <link>http://www.uwm.edu/~tofias/papers/demento.apsa08.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I am not surprised that Nominate is unstable under subsampling, but I had no idea it was _that_ unstable.
]]></description>
<dc:subject>congress nominate clustering statistics political_science latent_variables via:justin</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:688cbf1e5467/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:congress"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nominate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:justin"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.hiit.fi/~buntine/uai2004.html">
    <title>Applying Discrete PCA in Data Analysis</title>
    <dc:date>2009-05-01T11:59:36+00:00</dc:date>
    <link>http://www.hiit.fi/~buntine/uai2004.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I heard Alek talk about this at UAI 2004... and then forgot about it completely when I taught data mining.  My bad.
]]></description>
<dc:subject>to_teach:data-mining statistics latent_variables latent_semantic_analysis to:NB jakulin.aleks buntine.wray principal_components independent_component_analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a2eea9f875cc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_semantic_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jakulin.aleks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:buntine.wray"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:principal_components"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:independent_component_analysis"/>
</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/1121347636">
    <title>On Model Expansion, Model Contraction, Identifiability and Prior Information: Two Illustrative Scenarios Involving Mismeasured Variables</title>
    <dc:date>2009-03-27T23:51:47+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.ss/1121347636</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>statistics identifiability estimation via:guslacerda latent_variables to_read gustafson.paul</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aeae0e9588d5/</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:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:guslacerda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gustafson.paul"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0901.4785">
    <title>[0901.4785] Anomalous Diffusion and Scaling in Coupled Stochastic Processes</title>
    <dc:date>2009-02-02T17:00:22+00:00</dc:date>
    <link>http://arxiv.org/abs/0901.4785</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>stochastic_processes to:NB to_read latent_variables</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:82c51dc83700/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<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:latent_variables"/>
</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/1009210550">
    <title>Stratified exponential families: Graphical models and model selection</title>
    <dc:date>2009-01-11T20:22:43+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1009210550</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>graphical_models exponential_families latent_variables</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:65bc3eebc64a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:exponential_families"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780521844635">
    <title>Measuring the Mind: Conceptual Issues in Contemporary Psychometrics - Borsboom [@Labyrinth]</title>
    <dc:date>2008-08-21T01:46:29+00:00</dc:date>
    <link>http://www.labyrinthbooks.com/sale_detail.aspx?isbn=9780521844635</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Probably the best book available on the status of psychological measurements.  Micro-review with links at http://bactra.org/weblog/algae-2008-01.html
]]></description>
<dc:subject>books:recommended psychometrics philosophy_of_science latent_variables inference_to_latent_objects borsboom.denny</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:06cd484a9fd4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:borsboom.denny"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.cmu.edu/tr/tr750/tr750.html">
    <title>Unbiased Methods for Population-based Association Studies</title>
    <dc:date>2008-06-08T02:21:31+00:00</dc:date>
    <link>http://www.stat.cmu.edu/tr/tr750/tr750.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>genetics statistics confounding genomic_control regression latent_variables kith_and_kin devlin.bernie roeder.kathryn bacanu.silviu-alin</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c7a995c78457/</dc:identifier>
<taxo:topics><rdf:Bag>	<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:confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:genomic_control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:latent_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:devlin.bernie"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:roeder.kathryn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bacanu.silviu-alin"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>