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	<title>Comments on: Bayesian statistics is misnamed</title>
	<atom:link href="http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/feed/" rel="self" type="application/rss+xml" />
	<link>http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/</link>
	<description>The blog of John D. Cook</description>
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		<title>By: Doug Rubino</title>
		<link>http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/comment-page-1/#comment-17651</link>
		<dc:creator>Doug Rubino</dc:creator>
		<pubDate>Mon, 18 May 2009 08:04:26 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=2061#comment-17651</guid>
		<description>In light of Richard Cox&#039;s Theorem, which bring into equivalence Kolmogorov&#039;s axioms for probability theory and the Aristotelian (equivalently Boolean) notions of the AND, OR and NOT relations when extended to the continuum, if one were to use the word &#039;Statistics&#039;, I would probably go with Aristotelian Statistics, by virtue of reverence.

Yet, first order logic is &#039;Generalized Aristotelian Logic,&#039; but logicians certainly don&#039;t refer to it that way. Should a person&#039;s name be included in a theory, the theory is automatically weakened due to apparent arbitrariness. 

Should you truly believe that Bayesian Inference is the one and only way to do logic under uncertainty, call it &#039;The Theory of Inference&#039; or &#039;Inference Theory,&#039; or take a tip from logicians, and call it just plain &#039;Inference&#039;. Let other theories of inference compete, in the axiomatic sense, and see if it stands the test of time. 

Like all meaningful debates in binary logic, the debates of rational inference will all come down to the interpretation of the conditional, which it so happens is not the extension of material implication. Recall that one way to interpret Godel&#039;s second incompleteness theorem is that material implication does not equal deductive implication. Were they to be equal, then an unprovable proposition would vacuously be provable. Projecting this form of argument to the probabilistic conditional would certainly be the way I would go about assessing an axiomatization of inference.

-Doug</description>
		<content:encoded><![CDATA[<p>In light of Richard Cox&#8217;s Theorem, which bring into equivalence Kolmogorov&#8217;s axioms for probability theory and the Aristotelian (equivalently Boolean) notions of the AND, OR and NOT relations when extended to the continuum, if one were to use the word &#8216;Statistics&#8217;, I would probably go with Aristotelian Statistics, by virtue of reverence.</p>
<p>Yet, first order logic is &#8216;Generalized Aristotelian Logic,&#8217; but logicians certainly don&#8217;t refer to it that way. Should a person&#8217;s name be included in a theory, the theory is automatically weakened due to apparent arbitrariness. </p>
<p>Should you truly believe that Bayesian Inference is the one and only way to do logic under uncertainty, call it &#8216;The Theory of Inference&#8217; or &#8216;Inference Theory,&#8217; or take a tip from logicians, and call it just plain &#8216;Inference&#8217;. Let other theories of inference compete, in the axiomatic sense, and see if it stands the test of time. </p>
<p>Like all meaningful debates in binary logic, the debates of rational inference will all come down to the interpretation of the conditional, which it so happens is not the extension of material implication. Recall that one way to interpret Godel&#8217;s second incompleteness theorem is that material implication does not equal deductive implication. Were they to be equal, then an unprovable proposition would vacuously be provable. Projecting this form of argument to the probabilistic conditional would certainly be the way I would go about assessing an axiomatization of inference.</p>
<p>-Doug</p>
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		<title>By: Tom Hampton</title>
		<link>http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/comment-page-1/#comment-16268</link>
		<dc:creator>Tom Hampton</dc:creator>
		<pubDate>Tue, 21 Apr 2009 16:09:06 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=2061#comment-16268</guid>
		<description>Thanks very much for this clarification -- Bayes&#039; rule always seemed frequentist to me, and now I see it is not the dividing line between the two camps.</description>
		<content:encoded><![CDATA[<p>Thanks very much for this clarification &#8212; Bayes&#8217; rule always seemed frequentist to me, and now I see it is not the dividing line between the two camps.</p>
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		<title>By: bill r</title>
		<link>http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/comment-page-1/#comment-16264</link>
		<dc:creator>bill r</dc:creator>
		<pubDate>Tue, 21 Apr 2009 13:43:14 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=2061#comment-16264</guid>
		<description>Well, how about:
  Laplacian Statistics?
  Jeffriesian Statistics?
  Coxian/Jaynesian Statistics? (Big hint as to what I&#039;ve been reading lately...)</description>
		<content:encoded><![CDATA[<p>Well, how about:<br />
  Laplacian Statistics?<br />
  Jeffriesian Statistics?<br />
  Coxian/Jaynesian Statistics? (Big hint as to what I&#8217;ve been reading lately&#8230;)</p>
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		<title>By: John</title>
		<link>http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/comment-page-1/#comment-16262</link>
		<dc:creator>John</dc:creator>
		<pubDate>Tue, 21 Apr 2009 13:08:24 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=2061#comment-16262</guid>
		<description>See this &lt;a href=&quot;http://www.stat.columbia.edu/~cook/movabletype/archives/2008/10/bayes_bayesians.html&quot; rel=&quot;nofollow&quot;&gt;post&lt;/a&gt; by Andrew Gelman elaborating on his comment above.</description>
		<content:encoded><![CDATA[<p>See this <a href="http://www.stat.columbia.edu/~cook/movabletype/archives/2008/10/bayes_bayesians.html" rel="nofollow">post</a> by Andrew Gelman elaborating on his comment above.</p>
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		<title>By: Andrew Gelman</title>
		<link>http://www.johndcook.com/blog/2009/04/20/bayesian-statistics-is-misnamed/comment-page-1/#comment-16260</link>
		<dc:creator>Andrew Gelman</dc:creator>
		<pubDate>Tue, 21 Apr 2009 13:01:20 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=2061#comment-16260</guid>
		<description>Every statistician, from R.A. Fisher on, uses Bayesian inference when it&#039;s appropriate.  What makes a Bayesian a Bayesian is that he or she uses Bayesian inference when it&#039;s inappropriate as well.  (And, yes, I&#039;m a Bayesian.)</description>
		<content:encoded><![CDATA[<p>Every statistician, from R.A. Fisher on, uses Bayesian inference when it&#8217;s appropriate.  What makes a Bayesian a Bayesian is that he or she uses Bayesian inference when it&#8217;s inappropriate as well.  (And, yes, I&#8217;m a Bayesian.)</p>
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