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	<title>Comments on: FFT: Friedman + Fortran + Tricks</title>
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	<link>http://brenocon.com/blog/2009/07/fft-friedman-fortran-tricks/</link>
	<description>cognition, language, social systems; statistics, visualization, computation</description>
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		<title>By: Bob Carpenter</title>
		<link>http://brenocon.com/blog/2009/07/fft-friedman-fortran-tricks/#comment-8408</link>
		<dc:creator>Bob Carpenter</dc:creator>
		<pubDate>Wed, 22 Jul 2009 17:01:48 +0000</pubDate>
		<guid isPermaLink="false">http://anyall.org/blog/?p=602#comment-8408</guid>
		<description><![CDATA[I really like their prior vs. coefficient line graphs (as in their book).  I&#039;ve been thinking about implementing the coordinate descent algorithm ever since I looked over Genken, Lewis and Madigan&#039;s &lt;a href=&quot;http://www.bayesianregression.org/&quot; rel=&quot;nofollow&quot;&gt;Bayesian Regression&lt;/a&gt; package.  

Another reason people still like Fortran is that it&#039;s such a simple language that the loops are easy to automatically parallelize.  

Somehwat counterintuitively, languages in the ML family are also super-fast at simple matrixes because of the ability of the compiler to statically optimize.]]></description>
		<content:encoded><![CDATA[<p>I really like their prior vs. coefficient line graphs (as in their book).  I&#8217;ve been thinking about implementing the coordinate descent algorithm ever since I looked over Genken, Lewis and Madigan&#8217;s <a href="http://www.bayesianregression.org/" rel="nofollow">Bayesian Regression</a> package.  </p>
<p>Another reason people still like Fortran is that it&#8217;s such a simple language that the loops are easy to automatically parallelize.  </p>
<p>Somehwat counterintuitively, languages in the ML family are also super-fast at simple matrixes because of the ability of the compiler to statically optimize.</p>
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