<story><title>The Algorithms Behind Probabilistic Programming</title><url>http://blog.fastforwardlabs.com/2017/01/30/the-algorithms-behind-probabilistic-programming.html</url></story><parent_chain></parent_chain><comment><author>sn6uv</author><text>Nit&lt;p&gt;&amp;gt; The distribution must be smooth (this method doesn’t work for discrete parameters), but it need not be analytically differentiable&lt;p&gt;&amp;#x27;Smooth&amp;#x27; and &amp;#x27;analytic&amp;#x27; both have basic technical meanings in the context of differentiability [1], [2].&lt;p&gt;Glossing over the assumptions in this way is quite misleading&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Smoothness&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Smoothness&lt;/a&gt;
[2] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Analytic_function&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.m.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Analytic_function&lt;/a&gt;</text></comment>
<story><title>The Algorithms Behind Probabilistic Programming</title><url>http://blog.fastforwardlabs.com/2017/01/30/the-algorithms-behind-probabilistic-programming.html</url></story><parent_chain></parent_chain><comment><author>joe_the_user</author><text>Nice,&lt;p&gt;The algorithms the article define seem to justify seeing probabilistic programming as an advance beyond twenty line programs that call 20K sized libraries - basically, these algorithm inherently auto-tune a lot of higher level operations so these can be less-leaky abstractions.</text></comment>