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    <title>Skilling : Nested sampling for general Bayesian computation</title>
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    <meta name="citation_author" content="John Skilling"/>
    <meta name="citation_title" content="Nested sampling for general Bayesian computation"/>
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    <div class="main-content"><div class="container"><div class="thirteen columns"><div class="gutter"><h2>Bayesian Analysis</h2><ul class="nav-pills"><li><a href="/info/euclid.ba">Info</a></li><li><a href="/adv/euclid.ba">Advance publication</a></li><li><a href="/current/euclid.ba">Current issue</a></li><li><a href="/all/euclid.ba">All issues</a></li><li><a href="/search/euclid.ba">Search</a></li></ul><section class="publication-content"><ul class="pager"><div><li><a href="/euclid.ba/1340370943"> &#x2190; Previous article</a></li><li><a href="/euclid.ba/1340370931#toc">TOC</a></li><li><a href="/euclid.ba/1340370945">Next article &#x2192; </a></li></div> </ul><ul class="citation"><li><a href="/euclid.ba">Bayesian Anal.</a></li><li> Volume 1, Number 4 (2006), 833-859.</li></ul><h3>Nested sampling for general Bayesian computation</h3><p class="small"><a class="trigger" href="#author-euclidba1340370944SkillingJohn">John Skilling</a><div class="popover popover-before-after-applied" style="display: none; " id="author-euclidba1340370944SkillingJohn"><span role="presentation" class="before"> </span><h5>More by John Skilling</h5><p>Search this author in:</p><ul><li><a href="https://scholar.google.com/scholar?as_q=&amp;as_epq=&amp;as_oq=&amp;as_eq=&amp;as_occt=any&amp;as_sauthors=John+Skilling&amp;as_publication=&amp;as_ylo=&amp;as_yhi=&amp;btnG=&amp;hl=en&amp;as_sdt=0%2C33#">Google Scholar</a></li><li><a href="/search_result?type=index&amp;q.a.author=John%20Skilling">Project Euclid</a></li></ul><span role="presentation" class="after"> </span></div></p><div class="download-buttons box"><p><strong>Full-text: Open access</strong><div class="download-buttons"> <a class="btn btn-primary" href="/download/pdf_1/euclid.ba/1340370944"><i class="icon-pdf"> </i>PDF File (323 KB)</a>       </div></p></div><div class="tab-set"><ul class="nav-tabs"><li><a href="#abstract">Abstract</a></li><li><a href="#info">Article info and citation</a></li><li><a href="/first_page/euclid.ba/1340370944">First page</a></li></ul><div id="abstract" class="abstract"><h4>Abstract</h4><div class="abstract-text">
          <p>Nested sampling estimates directly how the likelihood function relates to prior mass.
            The evidence (alternatively the marginal likelihood, marginal density of the data, or
            the prior predictive) is immediately obtained by summation. It is the prime result of
            the computation, and is accompanied by an estimate of numerical uncertainty. Samples
            from the posterior distribution are an optional by-product, obtainable for any
            temperature. The method relies on sampling within a hard constraint on likelihood value,
            as opposed to the softened likelihood of annealing methods. Progress depends only on the
            shape of the "nested" contours of likelihood, and not on the likelihood values. This
            invariance (over monotonic re-labelling) allows the method to deal with a class of
            phase-change problems which effectively defeat thermal annealing.</p>
        </div></div><div id="info"><h4>Article information</h4><p><strong>Source</strong><br/><a href="/euclid.ba">Bayesian Anal.</a>  Volume 1, Number 4 (2006), 833-859.</p><p><strong>Dates</strong><br/>First available in Project Euclid:  22 June 2012<br/></p><p><strong>Permanent link to this document</strong><br/>https://projecteuclid.org/euclid.ba/1340370944</p><p><strong>Digital Object Identifier</strong><br/>doi:10.1214/06-BA127</p><p><strong>Mathematical Reviews number (MathSciNet)</strong><br/><a href="https://mathscinet.ams.org/mathscinet-getitem?mr=2282208"> MR2282208</a><br/></p><p><strong>Zentralblatt MATH identifier</strong><br/><a href="https://zbmath.org/?q=an:1332.62374">1332.62374</a></p><p class="keywords"><strong>Keywords</strong><br/><a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=Bayesian+computation&amp;linkedSearch=1">Bayesian computation</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=evidence&amp;linkedSearch=1">evidence</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=marginal+likelihood&amp;linkedSearch=1">marginal likelihood</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=algorithm&amp;linkedSearch=1">algorithm</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=nest&amp;linkedSearch=1">nest</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=annealing&amp;linkedSearch=1">annealing</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=phase+change&amp;linkedSearch=1">phase change</a> <a title="Search this keyword in Euclid" href="/search_result?type=index&amp;q.a.keyword=model+selection&amp;linkedSearch=1">model selection</a> </p><h4>Citation</h4><div id="printer-citation"><p><span id="dynamic_citation">Skilling, John. Nested sampling for general Bayesian computation. 
 Bayesian Anal. 1 (2006), no. 4, 833--859. doi:10.1214/06-BA127. https://projecteuclid.org/euclid.ba/1340370944</span></p></div><br/><div id="export" class="modal hide fade" tabindex="-1" role="dialog" aria-labelledby="moreFacets" aria-hidden="true"><div class="modal-header"><a data-dismiss="modal" aria-hidden="true"><i class="icon-remove"> </i></a><h3 id="myModalLabel">Export citation</h3></div><div class="modal-body"><h4>Format:</h4><select id="export-format-select"><option value="bibtex">Text (BibTeX)</option><option value="printer">Text (printer-friendly)</option><option value="ris">RIS (EndNote, ProCite, Reference Manager)</option></select><h4>Delivery Method:</h4><input type="radio" name="delivery" value="browser" checked="true">Download</input>  <input type="radio" name="delivery" value="email">Email</input>  <input type="text" name="modal-address" disabled="true"/><br/><br/><div class="alert alert-error hidden">Please enter a valid email address.</div><div class="alert alert-success hidden">Email sent.</div></div><div class="modal-footer"><button id="export-button" class="btn btn-primary"><i class="icon-share"> </i> Export</button><button class="btn" data-dismiss="modal" aria-hidden="true">Cancel</button></div></div><form id="export-form" method="post" action="/export_citations" enctype="application/x-www-form-urlencoded"><input id="export-format" name="format" value="bibtex" type="hidden"/><input id="export-delivery" name="delivery" value="browser" type="hidden"/><input id="export-address" name="address" type="hidden"/><input name="h" class="item_toggle" type="hidden" checked="checked" id="euclid.ba/1340370944" value="euclid.ba/1340370944"/><a class="btn export-link-special" href="#export" data-toggle="modal"><i class="icon-share"> </i> Export citation</a></form></div></div></section></div></div><div class="three columns sidebar"><div class="gutter"><div class="publisher-logo"><a href="/publisher/euclid.publisher.isba"><img src="/publisher/images/publisher_isba.png"/></a></div><div class="sidebar-box publisher-nav"><h4><a href="/publisher/euclid.publisher.isba">International Society for Bayesian Analysis</a></h4><ul><li><a href="http://www.e-publications.org/ims/submission/BA/help/about/">Editorial Board</a></li><li><a href="/authors/euclid.ba">For Authors</a></li><li><a href="/subscriptions/euclid.ba">Subscriptions</a></li></ul><ul><li><a href="/firstOnline/euclid.ba">First Online</a></li><li><a href="/policy/euclid.ba">Journal Copyright Policy</a></li><li><a href="/publisher/euclid.publisher.ims">IMS Supported Journal</a></li></ul></div><div class="sidebar-box sidebar-cover"><img class="border-gray" src="/publication/euclid.ba/images/cover_ba_111.gif" alt="Bayesian Analysis"/></div><div class="sidebar-box journal-tracking"><h3>New content alerts</h3><a href="/login?returnArgs=addEmailToc#myeuclid" class="btn"><i class="icon-envelope"> </i>
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