<?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="http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/evans.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1301.3863"/>
	<rdf:li rdf:resource="http://jmlr.csail.mit.edu/papers/v9/lebanon08a.html"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/evans.pdf">
    <title>Recovery from selection bias using marginal structure in discrete models</title>
    <dc:date>2015-07-16T14:21:43+00:00</dc:date>
    <link>http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/evans.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper considers the problem of inferring a discrete joint distribution from a sample subject to selection. Abstractly, we want to identify a distribution p(x, w) from its condi- tional p(x | w). We introduce new assump- tions on the marginal model for p(x), un- der which generic identification is possible. These assumptions are quite general and can easily be tested; they do not require pre- cise background knowledge of p(x) or p(w), such as proportions estimated from previous studies. We particularly consider conditional independence constraints, which often arise from graphical and causal models, although other constraints can also be used. We show that generic identifiability of causal effects is possible in a much wider class of causal mod- els than had previously been known."]]></description>
<dc:subject>graphical_models identifiability statistics categorical_data partial_identification algebra heard_the_talk didelez.vanessa in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cd6ef048a15b/</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:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:categorical_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partial_identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algebra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:didelez.vanessa"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.3863">
    <title>[1301.3863] YGGDRASIL - A Statistical Package for Learning Split Models</title>
    <dc:date>2013-01-20T19:00:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.3863</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There are two main objectives of this paper. The first is to present a statistical framework for models with context specific independence structures, i.e., conditional independences holding only for sepcific values of the conditioning variables. This framework is constituted by the class of split models. Split models are extension of graphical models for contigency tables and allow for a more sophisticiated modelling than graphical models. The treatment of split models include estimation, representation and a Markov property for reading off those independencies holding in a specific context. The second objective is to present a software package named YGGDRASIL which is designed for statistical inference in split models, i.e., for learning such models on the basis of data."]]></description>
<dc:subject>to:NB statistics graphical_models categorical_data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1e7db9ea86b3/</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:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:categorical_data"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v9/lebanon08a.html">
    <title>Non-Parametric Modeling of Partially Ranked Data</title>
    <dc:date>2012-02-05T18:54:04+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v9/lebanon08a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Statistical models on full and partial rankings of n items are often of limited practical use for large n due to computational consideration. We explore the use of non-parametric models for partially ranked data and derive computationally efficient procedures for their use for large n. The derivations are largely possible through combinatorial and algebraic manipulations based on the lattice of partial rankings. A bias-variance analysis and an experimental study demonstrate the applicability of the proposed method."]]></description>
<dc:subject>to:NB statistics machine_learning categorical_data ordinal_data information_retrieval nonparametrics lebanon.guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:44482224cc87/</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:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:categorical_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ordinal_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lebanon.guy"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>