Speaking of which, check out Michael I. Jordans work on Probabilistic Graphical Models <a href="https://www.google.com/search?q=michael+i+jordan+probalistic+prrogramming&oq=michael+i+jordan+probalistic+prrogramming&aqs=chrome..69i57.17982j0j4&sourceid=chrome&ie=UTF-8" rel="nofollow">https://www.google.com/search?q=michael+i+jordan+probalistic...</a><p>Mentor to Andrew Ng, former head of Google AI, Baidu and a few other things. <a href="https://en.wikipedia.org/wiki/Michael_I._Jordan" rel="nofollow">https://en.wikipedia.org/wiki/Michael_I._Jordan</a><p>Saira, Mina and David worked on some interesting stuff related to using ML/AI in extending human life span, nematodes a while back. Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span - Blei DM, Franks K, Jordan MI, Mian IS. - <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868" rel="nofollow">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1533868</a>
There is also ZhuSuan which also leverages TensorFlow.<p><a href="https://arxiv.org/abs/1709.05870v1" rel="nofollow">https://arxiv.org/abs/1709.05870v1</a><p><a href="https://github.com/thu-ml/zhusuan" rel="nofollow">https://github.com/thu-ml/zhusuan</a><p><a href="https://i.imgur.com/gzfhS29.png" rel="nofollow">https://i.imgur.com/gzfhS29.png</a>
The software library is located here: <a href="http://edwardlib.org/" rel="nofollow">http://edwardlib.org/</a> . Notably, Edward is layered on TensorFlow.<p>Regarding the significance of the authors, David Blei first described latent Dirichlet allocation (LDA), an important algorithm for generative topic modeling, in ~2003. Interestingly, the last I checked, LDA couldn't be done in Edward (yet).
I'll have to read the paper to see what makes it "deep"...<p>A cursory skim suggests that it is much faster than Stan, but I suppose the more significant question is if it provides the correct results. Stan might take longer, but I'm usually pretty confident that with some simple diagnostics I can see whether the results are what I really need.
A nice beginner friendly book about Probabilistic Programming is the book by Avi Pfeffer: "Practical Probabilistic Programming" (published by Manning). The only downside of the book is that it used Pfeffer's own Scala library called Figaro, which does not seem to get as much attention as projects such as Stan and Edward.
Anyone recommend any good resources for learning to use Edward?<p>The tutorials on the main site are good? <a href="http://edwardlib.org/tutorials/" rel="nofollow">http://edwardlib.org/tutorials/</a>
There is another Tensorflow bayesian programming library called Aboleth - <a href="https://github.com/data61/aboleth" rel="nofollow">https://github.com/data61/aboleth</a>
#not related but:<p>Maybe nobody else cares, but the name does matter. Edward, Stan, Cassandra. Have we run out of computer (or programming) sounding names?<p>This is Computatrum Antropomorphicus.