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    <title>[1402.4914] Building fast Bayesian computing machines out of intentionally stochastic, digital parts</title>
    <dc:date>2017-02-14T17:22:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1402.4914</link>
    <dc:creator>arthegall</dc:creator><dc:subject>vikash-mansinghka eric-jonas neuroscience arxiv research-article bayesian-methods computers via:vaguery</dc:subject>
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    <title>Edward – Home</title>
    <dc:date>2016-11-30T18:45:13+00:00</dc:date>
    <link>http://edwardlib.org/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["A library for probabilistic modeling, inference, and criticism." ]]></description>
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    <title>Ghitza, Gelman, &quot;Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups&quot;</title>
    <dc:date>2016-11-12T13:14:39+00:00</dc:date>
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    <dc:creator>arthegall</dc:creator><description><![CDATA[I wonder how much of this still applies post-2016 elections...]]></description>
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    <title>The Population Posterior and Bayesian Modeling on Streams</title>
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    <title>[1602.05221] Patterns of Scalable Bayesian Inference</title>
    <dc:date>2016-02-19T11:17:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1602.05221</link>
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    <title>Bayesian Optimization with Inequality Constraints</title>
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    <title>Vincent Crawford</title>
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    <title>Hennig, Osborne, Girolami, &quot;Probabilistic Numerics and Uncertainty in Computations&quot;</title>
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    <title>[1306.2365] Bayesian Uncertainty Quantification for Differential Equations</title>
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    <dc:creator>arthegall</dc:creator><dc:subject>via:? graphical-models arxiv research-article bayesian-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:dfb8a85263f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:graphical-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://probcomp.csail.mit.edu/bayesdb/">
    <title>BayesDB</title>
    <dc:date>2013-12-01T14:48:36+00:00</dc:date>
    <link>http://probcomp.csail.mit.edu/bayesdb/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Pat's bayesian database project.]]></description>
<dc:subject>pat-shafto database bayesian-methods inference query-language project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:7066a7d53f00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:pat-shafto"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:query-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.0911">
    <title>[1309.0911] A Bayesian information criterion for singular models</title>
    <dc:date>2013-09-29T14:24:27+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.0911</link>
    <dc:creator>arthegall</dc:creator><dc:subject>bayesian-methods arxiv research-article bic statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:041608cd1c80/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lh3lh3.users.sourceforge.net/download/samtools.pdf">
    <title>Heng Li, &quot;Mathematical Notes on SAMtools Algorithms&quot;</title>
    <dc:date>2013-08-30T20:23:43+00:00</dc:date>
    <link>http://lh3lh3.users.sourceforge.net/download/samtools.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[I have been *literally* looking for a document like this for over a year. ]]></description>
<dc:subject>heng-li broad work algorithms bayesian-methods genomics variants sequence-analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:b101c73fd981/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:heng-li"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:broad"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:work"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:variants"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sequence-analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1307.7963">
    <title>[1307.7963] Efficient variational inference for generalized linear mixed models with large datasets</title>
    <dc:date>2013-08-20T10:41:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.7963</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:cshalizi variational-methods research-article linear-regression bayesian-methods arxiv</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:e05a82ab9f5b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:variational-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:linear-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.nature.com/nbt/journal/v26/n7/full/nbt1414.html">
    <title>A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis : Article : Nature Biotechnology</title>
    <dc:date>2013-06-28T14:55:28+00:00</dc:date>
    <link>http://www.nature.com/nbt/journal/v26/n7/full/nbt1414.html</link>
    <dc:creator>arthegall</dc:creator><dc:subject>genomics research-article saving methylation bayesian-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:72b0d5617689/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:saving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:methylation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.0598">
    <title>[1305.0598] Cost-Recovering Bayesian Algorithmic Mechanism Design</title>
    <dc:date>2013-06-28T11:23:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.0598</link>
    <dc:creator>arthegall</dc:creator><dc:subject>saving arxiv to-read research-article mechanism-design bayesian-methods auctions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:6e717a35126b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:saving"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mechanism-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:auctions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.4283">
    <title>[1305.4283] Statistical modelling of summary values leads to accurate Approximate Bayesian Computations</title>
    <dc:date>2013-06-22T10:03:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.4283</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:? statistics abc bayesian-methods approximation research-article machinelearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:e1bd1ceb46e5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:abc"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3219132/">
    <title>PNAS Plus: Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion</title>
    <dc:date>2013-01-06T12:38:47+00:00</dc:date>
    <link>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3219132/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[David Wheeler and Peter Park are senior authors on the paper...  this is the technique used by [ast least one of the analysis centers in] the TCGA project for CNA of NGS data.]]></description>
<dc:subject>peter-park tcga data bayesian-methods sequencing genomics ngs copy-number-analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:8f051a27ca86/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:peter-park"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:tcga"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sequencing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:genomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:ngs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:copy-number-analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.5518">
    <title>Cai, Daskalakis, Weinberg, &quot;Optimal Multi-Dimensional Mechanism Design: Reducing Revenue to Welfare Maximization&quot; arXiv (July 2012)</title>
    <dc:date>2013-01-06T11:38:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.5518</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["We provide a reduction from revenue maximization to welfare maximization in multi-dimensional Bayesian auctions with arbitrary (possibly combinatorial) feasibility constraints and independent bidders with arbitrary (possibly combinatorial) demand constraints, appropriately extending Myerson's result to this setting. "]]></description>
<dc:subject>bayesian-methods arxiv research-article mechanism-design welfare-maximization auctions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:ae7026656acb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mechanism-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:welfare-maximization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:auctions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://danroy.org/papers/LloOrbGhaRoy-NIPS-2012.pdf">
    <title>Lloyd, Orbanz, Ghahramani, Roy &quot;Random function priors for exchangeable arrays with applications to graphs and relational data&quot;</title>
    <dc:date>2012-12-20T17:51:07+00:00</dc:date>
    <link>http://danroy.org/papers/LloOrbGhaRoy-NIPS-2012.pdf</link>
    <dc:creator>arthegall</dc:creator><dc:subject>daniel-roy zoubin-ghahramani machinelearning nonparametric-methods bayesian-methods data-structures nips</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:701df55edc4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:daniel-roy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:zoubin-ghahramani"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nonparametric-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:data-structures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nips"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mc-stan.org/index.html">
    <title>Stan: Project Home</title>
    <dc:date>2012-12-06T11:08:55+00:00</dc:date>
    <link>http://mc-stan.org/index.html</link>
    <dc:creator>arthegall</dc:creator><dc:subject>mcmc R c++ tool software library bayesian-methods no-u-turn-sampler bugs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:3b4f7a544f3c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mcmc"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:c++"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:tool"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:no-u-turn-sampler"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bugs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.6386">
    <title>[1206.6386] How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing</title>
    <dc:date>2012-09-05T18:35:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6386</link>
    <dc:creator>arthegall</dc:creator><dc:subject>bayesian-methods machinelearning grading research-article arxiv tom-minka item-response-models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:ae441107502f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:grading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:tom-minka"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:item-response-models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.6838">
    <title>[1206.6838] Continuous Time Markov Networks</title>
    <dc:date>2012-09-05T18:34:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6838</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:cshalizi arxiv research-article daphne-koller nir-friedman machinelearning bayesian-methods graphical-models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:a3ae797460c0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:daphne-koller"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nir-friedman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:graphical-models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002624">
    <title>Gerger, Onderdonk, Bry, &quot;Inferring Dynamic Signatures of Microbes in Complex Host Ecosystems&quot; PLoS Computational Biology (June 2012)</title>
    <dc:date>2012-08-07T11:40:58+00:00</dc:date>
    <link>http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002624</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Georg!!]]></description>
<dc:subject>metagenomics research-article friends plos computational-biology time-series bayesian-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:de4e61ec6ece/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:metagenomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:friends"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:plos"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:computational-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:time-series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kellogg.northwestern.edu/faculty/weinstein/htm/Bayesian-Uncertainty.pdf">
    <title>Al-Najjar and Weinstein, &quot;A Bayesian Model of Risk and Uncertainty&quot; (PDF)</title>
    <dc:date>2012-07-01T12:27:46+00:00</dc:date>
    <link>http://www.kellogg.northwestern.edu/faculty/weinstein/htm/Bayesian-Uncertainty.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Via the "A Fine Theorem" blog.]]></description>
<dc:subject>bayesian-methods risk uncertainty pdf to-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:8c158e724ab7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:risk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:uncertainty"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:to-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.5862">
    <title>[1206.5862] Clusters and features from combinatorial stochastic processes</title>
    <dc:date>2012-06-28T13:32:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.5862</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["We not only illustrate the feature allocation representations with the canonical nonparametric Bayesian feature prior---the Indian buffet process or beta process---but also simultaneously discover new connections between the different representations for the Indian buffet process. We thereby bring the same level of completeness to the treatment of the Indian buffet that has previously been developed for the Chinese restaurant."]]></description>
<dc:subject>via:cshalizi machinelearning nonparametric-methods bayesian-methods arxiv research-article michael-jordon chinese-restaurant-process indian-buffet-process</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:640c6a7dcf76/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nonparametric-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:michael-jordon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:chinese-restaurant-process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:indian-buffet-process"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.5366">
    <title>Gelman, Robert, &quot;'Not only defended but also applied': The perceived absurdity of Bayesian inference&quot; (arXiv)</title>
    <dc:date>2012-06-23T11:05:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.5366</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["In the binomial model under consideration here, the prior comes into the posterior distribution only once, and the likelihood comes in n times. It is perhaps merely an accident of history that skeptics and subjectivists alike strain on the gnat of the prior distribution while swallowing the camel that is the likelihood. In any case, it is instructive that Feller saw this example as an indictment of Bayes (or at least of the uniform prior as a prior for “no advance knowledge”) rather than of the binomial distribution."]]></description>
<dc:subject>bayesian-methods andrew-gelman history arxiv review-article statistics via:vaguery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:ffb6fac8dd07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:review-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:vaguery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://normaldeviate.wordpress.com/2012/06/14/freedmans-neglected-theorem/">
    <title>&quot;Freedman’s Neglected Theorem&quot; (Normal Deviate)</title>
    <dc:date>2012-06-15T11:57:40+00:00</dc:date>
    <link>http://normaldeviate.wordpress.com/2012/06/14/freedmans-neglected-theorem/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Wasserman quoting Freedman, “ … it is easy to prove that for essentially any pair of Bayesians, each thinks the other is crazy.”]]></description>
<dc:subject>humor statistics bayesian-methods inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:62066c675738/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:humor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1203.3468">
    <title>[1203.3468] Bayesian Rose Trees</title>
    <dc:date>2012-05-04T10:10:34+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.3468</link>
    <dc:creator>arthegall</dc:creator><dc:subject>yee-whye-teh arxiv research-articles bayesian-methods machinelearning trees hierarchical-data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:2b6e7f72f445/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:yee-whye-teh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-articles"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:hierarchical-data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~gelman/research/unpublished/comp7.pdf">
    <title>Huang &amp; Gelman, &quot;Sampling for Bayesian computation with large datasets&quot;</title>
    <dc:date>2012-04-24T12:41:08+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~gelman/research/unpublished/comp7.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Why had I not read this already? ]]></description>
<dc:subject>sampling bayesian-methods andrew-gelman pdf research-article statistics machinelearning multilevel-modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:b99e3355c2a2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:multilevel-modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0811.4458v2">
    <title>[0811.4458v2] Learning Class-Level Bayes Nets for Relational Data</title>
    <dc:date>2011-12-08T13:12:13+00:00</dc:date>
    <link>http://arxiv.org/abs/0811.4458v2</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Ah, this is the "Join Bayes Nets" paper, reworked...]]></description>
<dc:subject>via:cshalizi bayesian-methods bayesian-networks arxiv research-article probabilistic-relational-models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:arthegall/b:4bfb719dbd90/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probabilistic-relational-models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.5366v1">
    <title>Gelman, Robert &quot;'Not only defended but also applied': A look back at Feller's take on Bayesian inference&quot; (arXiv)</title>
    <dc:date>2010-09-06T14:48:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.5366v1</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Gelman vs. Feller.
]]></description>
<dc:subject>william-feller andrew-gelman statistics history bayesian-methods arxiv</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:c759adf9ae7b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:william-feller"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://ergodicity.net/2010/07/19/david-blackwell-has-passed-away/">
    <title>David Blackwell has passed away « An Ergodic Walk</title>
    <dc:date>2010-07-20T13:36:54+00:00</dc:date>
    <link>http://ergodicity.net/2010/07/19/david-blackwell-has-passed-away/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["I’ll always remember what [Blackwell] told me when I handed him a draft of my thesis. “The best thing about Bayesians is that they’re always right.”"
]]></description>
<dc:subject>humor bayesian-methods david-blackwell quote statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:837e380be96f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:humor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:david-blackwell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:quote"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cscs.umich.edu/~crshalizi/weblog/664.html">
    <title>Praxis and Ideology in Bayesian Data Analysis</title>
    <dc:date>2010-06-28T14:52:59+00:00</dc:date>
    <link>http://cscs.umich.edu/~crshalizi/weblog/664.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["Edward Prescott forms a noteworthy exception: under the rubric of "calibration", he has elevated his conviction that his prior guesses are never wrong into a new principle of statistical estimation."  -- Cosma's snark about statisticians and economists is the funniest snark around.  We could all aspire to have wits that were as subtle and dry as his... (seriously).
]]></description>
<dc:subject>by:cshalizi bayesian-methods andrew-gelman statistics econometrics edward-prescott humor</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:7a50a22a068a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:by:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:edward-prescott"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:humor"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2010/06/the_problem_of.html?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+StatisticalModelingCausalInferenceAndSocialScience+(Statistical+Modeling%2C+Causal+Inference%2C+and+Social+Science)&amp;utm_content=Google+Reader">
    <title>&quot;The problem of overestimation of group-level variance parameters&quot; (Andrew Gelman)</title>
    <dc:date>2010-06-02T15:11:25+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2010/06/the_problem_of.html?utm_source=feedburner&amp;utm_medium=feed&amp;utm_campaign=Feed%3A+StatisticalModelingCausalInferenceAndSocialScience+(Statistical+Modeling%2C+Causal+Inference%2C+and+Social+Science)&amp;utm_content=Google+Reader</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["... and many statisticians are uncomfortable with shrinkage." -- (C'mon, Gelman, go for the easy joke!)
]]></description>
<dc:subject>puerile-humor your-mom-jokes shrinkage statistics bayesian-methods hierarchical-models variation</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:f55ae986e63e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:puerile-humor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:your-mom-jokes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:shrinkage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:hierarchical-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:variation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/math/0606315">
    <title>Hutter, &quot;Bayesian Regression of Piecewise Constant Functions&quot; (arXiv)</title>
    <dc:date>2009-08-28T13:36:37+00:00</dc:date>
    <link>http://arxiv.org/abs/math/0606315</link>
    <dc:creator>arthegall</dc:creator><dc:subject>thesis research-article arxiv bayesian-methods regression change-points</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:de06f4d6f16c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:thesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:change-points"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.openhealthmeasures.org/WinBUGS/index.html">
    <title>A Beginner Battles WinBUGS</title>
    <dc:date>2009-08-10T20:07:24+00:00</dc:date>
    <link>http://www.openhealthmeasures.org/WinBUGS/index.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[One user's notes...
]]></description>
<dc:subject>winbugs statistics bayesian-methods software notes via:arsyed</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:d1b302e0d30d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:winbugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:notes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:arsyed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://absolutely-regular.blogspot.com/2009/05/concentration-of-exchangeable-processes.html">
    <title>&quot;concentration of exchangeable processes&quot; (Absolutely Regular)</title>
    <dc:date>2009-05-27T12:18:22+00:00</dc:date>
    <link>http://absolutely-regular.blogspot.com/2009/05/concentration-of-exchangeable-processes.html</link>
    <dc:creator>arthegall</dc:creator><dc:subject>persi-diaconis probability exchangeability bayesian-methods mathematics</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:6198d6d0a92e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:persi-diaconis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:exchangeability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mathematics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://cscs.umich.edu/~crshalizi/weblog/606.html">
    <title>Some Bayesian Finger-Puzzle Exercises, or: Often Wrong, Never In Doubt</title>
    <dc:date>2009-04-28T17:59:54+00:00</dc:date>
    <link>http://cscs.umich.edu/~crshalizi/weblog/606.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[For you to mull over...
]]></description>
<dc:subject>bayesian-methods food-for-thought modeling statistics inference</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:bc6f23bbe187/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:food-for-thought"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
</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/1169571801">
    <title>Tai and Speed, &quot;A multivariate empirical Bayes statistic for replicated microarray time course data&quot; (Ann. Statist. vol. 34 no. 5 (2006))</title>
    <dc:date>2009-04-25T15:48:21+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1169571801</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["In this paper we derive one- and two-sample multivariate empirical Bayes statistics (the MB-statistics) to rank genes in order of interest from longitudinal replicated developmental microarray time course experiments."
]]></description>
<dc:subject>microarray-analysis time-series statistics terence-speed bayesian-methods bioinformatics research-article</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:4dedcb2eaf0c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:microarray-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:time-series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:terence-speed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0904.3132">
    <title>Belloni and Chernozhukov, &quot;Posterior Inference in Curved Exponential Families under Increasing Dimensions&quot; (arXiv)</title>
    <dc:date>2009-04-23T12:07:31+00:00</dc:date>
    <link>http://arxiv.org/abs/0904.3132</link>
    <dc:creator>arthegall</dc:creator><dc:subject>via:cshalizi exponential-families statistics graphical-models arxiv research-article bayesian-methods</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:bbaa1261441f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:exponential-families"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:graphical-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2009/04/in_a_comment_on.html">
    <title>&quot;Deciding between simpler and more complex hypotheses&quot; (Andrew Gelman)</title>
    <dc:date>2009-04-08T14:57:08+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2009/04/in_a_comment_on.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["When deciding between simpler and more complex hypotheses, I generally prefer the more complex hypothesis. When I choose the simpler hypothesis, I view this as a combination of labor-saving device and approximate Bayes, pooling a parameter estimate all the way to zero instead of merely pooling it most of the way. I certainly don't see Bayes factors having any relevance, given the oft-noted problem that Bayes factors can depend decisively on aspects of the prior distribution that have no influence on the posterior distribution under each of the individual models." -- That's right: *all* the tea.
]]></description>
<dc:subject>model-selection andrew-gelman bayesian-methods modeling inference statistics advice</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:bbbed178e48e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:model-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:advice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://delong.typepad.com/sdj/2009/03/cosma-shalizi-takes-me-to-probability-school-or-is-it-philosophy-school.html#comments">
    <title>&quot;Cosma Shalizi Takes Me to Probability School. Or Is It Philosophy School?&quot; (Brad DeLong)</title>
    <dc:date>2009-03-28T16:33:26+00:00</dc:date>
    <link>http://delong.typepad.com/sdj/2009/03/cosma-shalizi-takes-me-to-probability-school-or-is-it-philosophy-school.html#comments</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[File the comments under, "Department of Missing the Point."
]]></description>
<dc:subject>statistics humor cosma-shalizi brad-delong bayesian-methods inference philosophy probability</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:8007caea2340/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:humor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:cosma-shalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:brad-delong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0710.4536">
    <title>Gramacy, Lee, &quot;Bayesian treed Gaussian process models with an application to computer modeling&quot; arXiv [0710.4536]</title>
    <dc:date>2009-03-23T13:28:04+00:00</dc:date>
    <link>http://arxiv.org/abs/0710.4536</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["Motivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning."  -- Via a commenter at Andrew Gelman's blog.
]]></description>
<dc:subject>research-article arxiv bayesian-methods gaussian-processes trees</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:13dbe52c495c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:gaussian-processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:trees"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://radfordneal.wordpress.com/2009/03/07/does-coverage-matter/">
    <title>&quot;Does coverage matter?&quot; (Radford Neal’s blog)</title>
    <dc:date>2009-03-10T11:31:42+00:00</dc:date>
    <link>http://radfordneal.wordpress.com/2009/03/07/does-coverage-matter/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["I think part of the problem is that reports of experimental results should not be aimed at presenting conclusions, as may seem most natural from a Bayesian viewpoint, but rather at providing the information with which the readers may draw conclusions.  This may be the source of some objections to the prior distribution in Bayesian analysis, which can be seen as corrupting the objective presentation of the experimental results, even though frequentist methods like p-values are not suitable presentations either."
]]></description>
<dc:subject>statistics bayesian-methods frequentist-methods p-values radford-neal</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:404870c784aa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:frequentist-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:p-values"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:radford-neal"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://journal.sjdm.org/8708/jdm8708.pdf">
    <title>Chapman and Liu, &quot;Numeracy, Frequency, and Bayesian Reasoning&quot;</title>
    <dc:date>2009-02-25T20:31:33+00:00</dc:date>
    <link>http://journal.sjdm.org/8708/jdm8708.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A follow-up to that Gigerenzer and Hoffrage paper!
]]></description>
<dc:subject>bayesian-methods probability numeracy research-article via:cshalizi</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:d59254b83168/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:numeracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lingpipe-blog.com/2009/02/10/elkan-and-noto-2008-learning-classifiers-from-only-positive-and-unlabeled-data/">
    <title>&quot;Elkan and Noto (2008): Learning Classifiers from Only Positive and Unlabeled Data&quot; (LingPipe Blog)</title>
    <dc:date>2009-02-14T17:50:54+00:00</dc:date>
    <link>http://lingpipe-blog.com/2009/02/10/elkan-and-noto-2008-learning-classifiers-from-only-positive-and-unlabeled-data/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[For an idea about probabilistic models of path connectivity...
]]></description>
<dc:subject>machinelearning bayesian-methods nlp classification unlabeled-data paths</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:89239bbfde4d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:unlabeled-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:paths"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0004495">
    <title>Hu &amp; Qin, &quot;Query Large Scale Microarray Compendium Datasets Using a Model-Based Bayesian Approach with Variable Selection&quot; (PLoS ONE)</title>
    <dc:date>2009-02-13T20:20:58+00:00</dc:date>
    <link>http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0004495</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Nice title.
]]></description>
<dc:subject>variable-selection bayesian-methods plos-one research-article bioinformatics microarray-analysis</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:5930482f0d99/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:variable-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:plos-one"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:microarray-analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cog.brown.edu/~mj/Publications.htm">
    <title>Publications, Mark Johnson, Cognitive and Linguistic Sciences, Brown University</title>
    <dc:date>2009-02-06T17:35:08+00:00</dc:date>
    <link>http://www.cog.brown.edu/~mj/Publications.htm</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Saw Mark Johnson give a talk about "Adaptor Grammars" (man, that 'o' really bothers me) two days ago.  It turned out to be ... an extremely boring talk, although the idea itself seems modestly interesting and it included several reasonable animations of hierarchical Chinese Restaurant processes that were modestly illuminating.  At any rate, I sat in the back, doodled on my notebook, and started to idly wonder if issues of "frequentist consistency" for this sort of learning process had been examined (or were even worth examining) at all...
]]></description>
<dc:subject>statistics machinelearning bayesian-methods grammar nlp linguistics consistency nonparametric-methods mark-johnson chinese-restaurant-process</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:4a3c4ac989ef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:grammar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:consistency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nonparametric-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mark-johnson"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:chinese-restaurant-process"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2009/02/different-meani.html">
    <title>&quot;Different meanings of Bayesian statistics&quot; (Andrew Gelman)</title>
    <dc:date>2009-02-04T15:49:17+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2009/02/different-meani.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["Anyway, I posted the above discussion (basically, all except for the previous two paragraphs, to their blog and got the strangest comments. Not that people were saying anything wrong, just they were coming from a traditional theoretical computer science perspective. For them, Bayesian statistics is all about code lengths; for me it's all about hierarchical models. Which I guess is consistent with my original point. Still, it's frustrating for me (but perhaps frustrating to some of these people from the other side, that statisticians see Bayes as about models rather than philosophy and code lengths). I thought that communicating with econometricians and non-Bayesian statisticians was tough, but this is a whole new level of difficulty!"
]]></description>
<dc:subject>humor statistics bayesian-methods opinion andrew-gelman yudkowsky</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:697362b3340c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:humor"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:yudkowsky"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.inf.ed.ac.uk/sgwater/reading_list.html">
    <title>Sharon Goldwater's Bayesian language modeling reading list</title>
    <dc:date>2009-02-02T22:11:45+00:00</dc:date>
    <link>http://homepages.inf.ed.ac.uk/sgwater/reading_list.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A reading list that goes through (it appears) 2007, hitting most of the high points -- broad, but not overly deep.
]]></description>
<dc:subject>language list research bayesian-methods modeling nlp</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:d04838ed3c7d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:list"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lingpipe-blog.com/2009/01/12/naive-bayes-binomial-bags-of-words/">
    <title>&quot;Naive Bayes, Binomials, and Bags of Words&quot; (LingPipe Blog)</title>
    <dc:date>2009-01-19T13:07:42+00:00</dc:date>
    <link>http://lingpipe-blog.com/2009/01/12/naive-bayes-binomial-bags-of-words/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A basic review of naive Bayes modeling, bag of words representations, and all that.
]]></description>
<dc:subject>machinelearning bayesian-methods nlp learning probabilistic-methods naive-bayes</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:00a3178bf4f3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probabilistic-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:naive-bayes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2009/01/r-the-fud-argum.html">
    <title>&quot;R, the FUD argument, the self-cleaning oven, and how to you count &quot;users&quot;?&quot; (Andrew Gelman)</title>
    <dc:date>2009-01-09T16:35:14+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2009/01/r-the-fud-argum.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["Unit-testing for Bayesian models."
]]></description>
<dc:subject>bayesian-methods andrew-gelman programming testing r</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:a420c268b98e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:r"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://hunch.net/?p=482">
    <title>&quot;A NIPS paper&quot; (Machine Learning (Theory))</title>
    <dc:date>2008-12-08T20:27:44+00:00</dc:date>
    <link>http://hunch.net/?p=482</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["I’m interested in this beyond the application to word prediction because it is relevant to the general normalization problem: If you want to predict the probability of one of a large number of events, often you must compute a predicted score for all the events and then normalize, a computationally inefficient operation. The problem comes up in many places using probabilistic models, but I’ve run into it with high-dimensional regression. There are a couple workarounds for this computational bug: (1) approximate, (2) avoid, (3) [what this paper does] use a self-normalizing structure."
]]></description>
<dc:subject>machinelearning nips paper nlp partition-function bayesian-methods</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:0840b25fe2cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nips"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:partition-function"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://research.microsoft.com/~joaquinc/">
    <title>Joaquin Quiñonero Candela's HomePage</title>
    <dc:date>2008-11-23T15:56:37+00:00</dc:date>
    <link>http://research.microsoft.com/~joaquinc/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Q-C's publications -- I wish I could find a table of contents for his new book, though.
]]></description>
<dc:subject>people homepage researcher bayesian-methods dataset-shift inference publications</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:c3f89ff1f319/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:people"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:homepage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:researcher"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:dataset-shift"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:publications"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0811.1319">
    <title>Plangprasopchok &amp; Lerman, &quot;Modeling Social Annotation: a Bayesian Approach&quot;</title>
    <dc:date>2008-11-23T14:53:15+00:00</dc:date>
    <link>http://arxiv.org/abs/0811.1319</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A model for social data and tagging, yes.  But not a *social* model, and certainly not something that models the social aspect of the tagging.  It seems like an obvious generalization, no?
]]></description>
<dc:subject>tagging social anntotation research-article arxiv bayesian-methods</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:c70c9c787b3f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:tagging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:social"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:anntotation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2008/11/netflix_prize_s.html">
    <title>&quot;Netflix Prize scoring function isn't Bayesian&quot; (Aleks Jakulin)</title>
    <dc:date>2008-11-23T14:37:48+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2008/11/netflix_prize_s.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["Now, your model might choose not to make recommendations with controversial movies - but this won't help you on Netflix Prize - you're forced to make errors even when you know you're making them. (R)MSE is pre-probabilistic: it gives no advantage to a probabilistic model that's aware of its own uncertainty."
]]></description>
<dc:subject>bayesian-methods netflix prize rmse statistics inference</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:d7315d0937d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:netflix"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:prize"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:rmse"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lingpipe-blog.com/2008/09/05/hierarchical-bayesian-models-of-categorical-data-annotation/">
    <title>&quot;Hierarchical Bayesian Models of Categorical Data Annotation&quot; (LingPipe Blog)</title>
    <dc:date>2008-10-03T10:25:43+00:00</dc:date>
    <link>http://lingpipe-blog.com/2008/09/05/hierarchical-bayesian-models-of-categorical-data-annotation/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[A two-page writeup of his BUGS models.
]]></description>
<dc:subject>bugs model probabilistic-methods bayesian-methods categorical-data data nlp</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:e3320b1e2fd9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probabilistic-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:categorical-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://scienceblogs.com/goodmath/2008/09/bad_probability_and_economic_d.php">
    <title>&quot;Bad Probability and Economic Disaster; or How Ignoring Bayes Theorem Caused the Mess&quot; (Good Math, Bad Math)</title>
    <dc:date>2008-09-24T21:25:15+00:00</dc:date>
    <link>http://scienceblogs.com/goodmath/2008/09/bad_probability_and_economic_d.php</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Assuming that two events are independent when they really aren't *isn't* the same thing as "ignoring Bayes theorem."
]]></description>
<dc:subject>stupid bayesian-methods idiocy finance crisis politics probability</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:c552e810eb14/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:stupid"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:idiocy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:crisis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:probability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~gelman/research/unpublished/election5.pdf">
    <title>Lock &amp; Gelman, &quot;Bayesian Combination of State Polls and Election Forecasts&quot;</title>
    <dc:date>2008-09-22T17:22:17+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~gelman/research/unpublished/election5.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Andrew Gelman's new paper on election prediction using state-level polling.  Partial pooling from multilevel models.
]]></description>
<dc:subject>bayesian-methods pdf politics elections political-science polling research-article statistics multilevel-modeling</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:397d2f7b8745/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:elections"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:political-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:polling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:multilevel-modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2008/09/robin_hanson_an.html">
    <title>&quot;Robin Hanson and I discuss adjusting for variables you shouldn't adjust for (for example, adjusting grades given sex, race, or pre-test scores)&quot; (Andrew Gelman)</title>
    <dc:date>2008-09-12T10:38:59+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2008/09/robin_hanson_an.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[I read this, and I think: double-counting.  (Right?)
]]></description>
<dc:subject>statistics inference education bayesian-methods regression double-counting</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:e2da9c8fd6ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:double-counting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~cook/movabletype/archives/2008/09/bayesian_comput_1.html">
    <title>&quot;Bayesian computation in Java?&quot; (Andrew Gelman)</title>
    <dc:date>2008-09-10T10:48:36+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~cook/movabletype/archives/2008/09/bayesian_comput_1.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[I need to move fast(er).
]]></description>
<dc:subject>bayesian-methods sampling mcmc bugs java programming question</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:ca6d1b50ac6d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mcmc"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:java"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:question"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0806.3286v1">
    <title>Chipman, George, and McCulloch &quot;BART: Bayesian Additive Regression Trees&quot; (arXiv)</title>
    <dc:date>2008-08-25T13:21:57+00:00</dc:date>
    <link>http://arxiv.org/abs/0806.3286v1</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Linked to by Andrew Gelman, I think.  "Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms in particular, BART is defined by a statistical model: a prior and a likelihood. This approach enables full posterior inference including point and interval estimates of the unknown regression function as well as the marginal effects of potential predictors."
]]></description>
<dc:subject>regression-trees regression arxiv research-article machine-learning statistics nonparametric-methods bayesian-methods</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:82ee99fd5bbf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:regression-trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nonparametric-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0808.0572">
    <title>Bradley Efron, &quot;Microarrays, Empirical Bayes and the Two-Groups Model&quot; (arXiv)</title>
    <dc:date>2008-08-07T17:57:50+00:00</dc:date>
    <link>http://arxiv.org/abs/0808.0572</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["...high-throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classical theory had in mind. In these situations empirical Bayes information begins to force itself upon frequentists and Bayesians alike. The two-groups model is a simple Bayesian construction that facilitates empirical Bayes analysis. This article concerns the interplay of Bayesian and frequentist ideas in the two-groups setting, with particular attention focused on Benjamini and Hochberg's False Discovery Rate method."
]]></description>
<dc:subject>via:cshalizi microarrays statistics hypothesis-testing data datamining neyman-pearson bayesian-methods empirical-bayes arxiv review</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:ce1aa2fe6f19/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:microarrays"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:hypothesis-testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:datamining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:neyman-pearson"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:empirical-bayes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:arxiv"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:review"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.stat.columbia.edu/~gelman/research/published/badbayesresponsemain.pdf">
    <title>badbayesresponsemain.pdf</title>
    <dc:date>2008-08-01T01:27:41+00:00</dc:date>
    <link>http://www.stat.columbia.edu/~gelman/research/published/badbayesresponsemain.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Gelman's response to his own April Fools joke (in the journal of Bayesian Analysis no less), and its responses. "In a nutshell: Bayesian statistics is about making probability statements, frequentist statistics is about evaluating probability statements."
]]></description>
<dc:subject>bayesian-methods andrew-gelman pdf statistics journal-article opinion april-fools</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:fa3c0236ab0e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:andrew-gelman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:pdf"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:journal-article"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:april-fools"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www1.cs.columbia.edu/streaming/common/player.php?file=/streaming/2008-Spr/ghah/ghah.flv&amp;image=/streaming/2008-Spr/ghah/slide1.jpg&amp;linkname=lecture%20note&amp;linksrc=/streaming/2008-Spr/ghah/ghahramani.pdf">
    <title>Zoubin Ghahramani, &quot;Recent directions in nonparametric Bayesian machine learning&quot;</title>
    <dc:date>2008-07-27T20:37:40+00:00</dc:date>
    <link>http://www1.cs.columbia.edu/streaming/common/player.php?file=/streaming/2008-Spr/ghah/ghah.flv&amp;image=/streaming/2008-Spr/ghah/slide1.jpg&amp;linkname=lecture%20note&amp;linksrc=/streaming/2008-Spr/ghah/ghahramani.pdf</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[Video. Via Andrew Gelman's blog.  Dynamic programming equations on the third slide -- nice.
]]></description>
<dc:subject>bayesian-methods machinelearning video inference statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:2e8563690bd7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www-fis.iarc.fr/~martyn/software/jags/">
    <title>JAGS</title>
    <dc:date>2008-07-15T19:56:04+00:00</dc:date>
    <link>http://www-fis.iarc.fr/~martyn/software/jags/</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA["JAGS is Just Another Gibbs Sampler.  It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation  not wholly unlike BUGS."
]]></description>
<dc:subject>mcmc modeling research statistics software opensource bayesian-methods sampling</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:76abacae0548/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:mcmc"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:opensource"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:sampling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nlpers.blogspot.com/2008/06/evaluating-topic-models.html">
    <title>&quot;Evaluating topic models&quot; (natural language processing blog)</title>
    <dc:date>2008-06-12T21:22:53+00:00</dc:date>
    <link>http://nlpers.blogspot.com/2008/06/evaluating-topic-models.html</link>
    <dc:creator>arthegall</dc:creator><description><![CDATA[What *would* "topic models for the sake of topic models" look like?  "I must publish a paper on topic models, so that no one forgets the name 'Dirichlet'?"
]]></description>
<dc:subject>nlp topic-models machinelearning bayesian-methods dirichlet_processes</dc:subject>
<dc:identifier>https://pinboard.in/u:arthegall/b:6791781a8f63/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:topic-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:bayesian-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:arthegall/t:dirichlet_processes"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>