<?xml version="1.0" encoding="UTF-8"?>

<rdf:RDF
   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
   xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
   xmlns="http://purl.org/rss/1.0/"
   xmlns:dc="http://purl.org/dc/elements/1.1/"
   xmlns:prism="http://prismstandard.org/namespaces/1.2/basic/"
   xmlns:dcterms="http://purl.org/dc/terms/"

>
<channel rdf:about="http://www.citeulike.org/about">
<pubDate>Sat, 05 Jul 2008 22:58:50 BST</pubDate>


	<title>CiteULike: mshafiei's sampling</title>
	<description>CiteULike: mshafiei's sampling</description>


	<link>http://www.citeulike.org/user/mshafiei/tag/sampling</link>
	<dc:publisher>CiteULike.org</dc:publisher>
	<dc:language>en-gb</dc:language>
	<dc:rights>Copyright &#169; 2004-2008 citeulike.org</dc:rights>
	<items>
    <rdf:Seq>
        <rdf:li rdf:resource="http://www.citeulike.org/user/mshafiei/article/2517325"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/mshafiei/article/2462307"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/mshafiei/article/815714"/>

	</rdf:Seq>
	</items>
	</channel>


<item rdf:about="http://www.citeulike.org/user/mshafiei/article/2517325">
    <title>A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior</title>
    <link>http://www.citeulike.org/user/mshafiei/article/2517325</link>
    <description>&lt;i&gt;J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577.&lt;/i&gt;</description>
    <dc:title>A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior</dc:title>

    <dc:creator>Hal Iii</dc:creator>
    <dc:creator>Daniel Marcu</dc:creator>
    <dc:source>J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577.</dc:source>
    <dc:date>2008-03-12T01:37:16-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:publicationName>J. Mach. Learn. Res.</prism:publicationName>
    <prism:issn>1533-7928</prism:issn>
    <prism:volume>6</prism:volume>
    <prism:startingPage>1551</prism:startingPage>
    <prism:endingPage>1577</prism:endingPage>
    <prism:publisher>MIT Press</prism:publisher>
    <prism:category>bayesian</prism:category>
    <prism:category>clustering</prism:category>
    <prism:category>conjugate</prism:category>
    <prism:category>crp</prism:category>
    <prism:category>mcmc</prism:category>
    <prism:category>non-conjugate</prism:category>
    <prism:category>nonparametric</prism:category>
    <prism:category>prior</prism:category>
    <prism:category>sampling</prism:category>
    <prism:category>supervised</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/mshafiei/article/2462307">
    <title>Nested sampling for Potts models</title>
    <link>http://www.citeulike.org/user/mshafiei/article/2462307</link>
    <description>&lt;i&gt;(2006), pp. 947-954.&lt;/i&gt;</description>
    <dc:title>Nested sampling for Potts models</dc:title>

    <dc:creator>Iain Murray</dc:creator>
    <dc:creator>David Mackay</dc:creator>
    <dc:creator>Zoubin Ghahramani</dc:creator>
    <dc:creator>John Skilling</dc:creator>
    <dc:source>(2006), pp. 947-954.</dc:source>
    <dc:date>2008-03-03T22:15:17-00:00</dc:date>
    <prism:publicationYear>2006</prism:publicationYear>
    <prism:startingPage>947</prism:startingPage>
    <prism:endingPage>954</prism:endingPage>
    <prism:publisher>MIT Press</prism:publisher>
    <prism:category>bayesian</prism:category>
    <prism:category>mcmc</prism:category>
    <prism:category>sampling</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/mshafiei/article/815714">
    <title>An introduction to MCMC for machine learning</title>
    <link>http://www.citeulike.org/user/mshafiei/article/815714</link>
    <description>&lt;i&gt;(2003)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.</description>
    <dc:title>An introduction to MCMC for machine learning</dc:title>

    <dc:creator>C Andrieu</dc:creator>
    <dc:creator>N de Freitas</dc:creator>
    <dc:creator>A Doucet</dc:creator>
    <dc:creator>M Jordan</dc:creator>
    <dc:source>(2003)</dc:source>
    <dc:date>2006-08-24T15:04:55-00:00</dc:date>
    <prism:publicationYear>2003</prism:publicationYear>
    <prism:category>learning</prism:category>
    <prism:category>machinelearning</prism:category>
    <prism:category>markov-chain</prism:category>
    <prism:category>mcmc</prism:category>
    <prism:category>sampling</prism:category>
    <prism:category>tutorial</prism:category>
</item>



</rdf:RDF>

