<story><title>Pyro: PyTorch-Based Deep Universal Probabilistic Programming</title><url>http://pyro.ai/</url></story><parent_chain></parent_chain><comment><author>ineedasername</author><text>&amp;quot;Deep&amp;quot; probabilistic? A quick search &amp;amp; it seems the term &amp;quot;Deep Probabilistic&amp;quot; was coined by the package author. Which, hey, nice work and all, but &amp;quot;deep&amp;quot; in this context looks like pure marketing fluff.&lt;p&gt;Maybe it&amp;#x27;s not, I&amp;#x27;m no longer cutting edge on this stuff-- my grad school days were a decade ago and the day job ::sigh:: doesn&amp;#x27;t require the more interesting stuff.&lt;p&gt;But I&amp;#x27;m gonna get a bit &amp;quot;get off my lawn&amp;quot; on this one and say that, in my day (woohoo!), neural nets could be deep; they had hidden depths (&amp;amp; layers). Belief networks could be deep, and they were adding depth to learning too; Much of the &amp;quot;deep&amp;quot; stuff today seems to use that word the same way Tide &amp;amp; OxyClean have &amp;quot;deep&amp;quot; cleaning technology in their laundry detergent.&lt;p&gt;All of which is to say, this is a question from someone in the early stages of cruftiness, meant in good humor, to ask &amp;quot;What makes them there probablistics &amp;#x27;deep&amp;#x27; ?&amp;quot; :)</text></comment>
<story><title>Pyro: PyTorch-Based Deep Universal Probabilistic Programming</title><url>http://pyro.ai/</url></story><parent_chain></parent_chain><comment><author>juxtaposicion</author><text>How does this compare to Edward, PyMC, Stan, et al? Is the primary distinction due to PyTorch’s imperative, dynamic programming?</text></comment>