I had never heard of probabilistic programming languages and was intrigued but unfortunately also rather confused by the submission. So thank you for the excellent pointer.
Anyway, I wrote this post as a precursor to a more substantial set of writings explaining how to practically use these upcoming languages. Most of what I am trying to show here is that while we use specialized algorithms for most of our machine learning at this moment, it is only a temporary solution before it becomes feasible to just specify your models in one of these languages. The point of the code is to show that many really complicated models with high specialized libraries for just them is less than 10 lines of code in this framework.
I never realized this article would be read by regular folks and am incredibly confused why something I wrote 6 months ago is suddenly all over the internet now.
This is an amazing talk by Tenenbaum. Thank you for posting it!
I'd really like to subscribe to your blog/site so I can read your more substantial set of writings as they appear -- but I can't seem to find the appropriate RSS or ATOM feed on your site. Am I missing something?
Any decent RSS reader (e.g. Google Reader, TTRSS) would then detect the feed if the user tried to subscribe to your homepage. Some browsers and browser extensions also provide a one-click subscribe button based on that tag.
It's also common to just put a visible link somewhere on the page, since not everyone knows about auto-discovery.
Stan aims to be the successor to BUGS, and it is made in part by the great Bayesian Andrew Gelman. Nevertheless, BUGS was the de facto language for probabilistic modelling for many years, and you may find better documentation for it.
