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    <title>topic Re: Overall log likelihood of a hierarchical bayes model in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-log-likelihood-of-a-hierarchical-bayes-model/m-p/317776#M16735</link>
    <description>&lt;P&gt;you could try holding all parameter estimates to the values you estimate using the 3 model frameworks in a framework that can nest all 3 specifications and outputs a true (not psuedo) likelihood. Maybe proc glimmix with method=laplace might work, depending on your original procedures.&lt;/P&gt;</description>
    <pubDate>Fri, 09 Dec 2016 03:08:19 GMT</pubDate>
    <dc:creator>Damien_Mather</dc:creator>
    <dc:date>2016-12-09T03:08:19Z</dc:date>
    <item>
      <title>Overall log likelihood of a hierarchical bayes model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-log-likelihood-of-a-hierarchical-bayes-model/m-p/306561#M16256</link>
      <description>&lt;P&gt;Hi everyone,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I want to compare the overall if of an MNL model, a fixed effects Bayes model and a random-effects hierarchical Bayes model. The log likelihood measure seems to be most appropirate for that, however how do I calculate the overall log likelihood for the HB model in the PROC BCHOICE procedure?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thank you for your help.&lt;/P&gt;&lt;P&gt;&lt;BR /&gt;Best,&lt;/P&gt;&lt;P&gt;Alex&lt;/P&gt;</description>
      <pubDate>Sat, 22 Oct 2016 14:47:00 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Overall-log-likelihood-of-a-hierarchical-bayes-model/m-p/306561#M16256</guid>
      <dc:creator>ahoeweler</dc:creator>
      <dc:date>2016-10-22T14:47:00Z</dc:date>
    </item>
    <item>
      <title>Re: Overall log likelihood of a hierarchical bayes model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Overall-log-likelihood-of-a-hierarchical-bayes-model/m-p/317776#M16735</link>
      <description>&lt;P&gt;you could try holding all parameter estimates to the values you estimate using the 3 model frameworks in a framework that can nest all 3 specifications and outputs a true (not psuedo) likelihood. Maybe proc glimmix with method=laplace might work, depending on your original procedures.&lt;/P&gt;</description>
      <pubDate>Fri, 09 Dec 2016 03:08:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Overall-log-likelihood-of-a-hierarchical-bayes-model/m-p/317776#M16735</guid>
      <dc:creator>Damien_Mather</dc:creator>
      <dc:date>2016-12-09T03:08:19Z</dc:date>
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