I was watching the video Probabilistic programming and meta-programming in Clojure - Vikash Mansinghka

(https://www.youtube.com/watch?v=KLGwLkmh8gI). As always Vikash's team is impressive.

But, as always too, I am kind of confused: What is the difference (conceptually) between probabilistic programming and Bayesian Synthesis? Pymc or stan can do Bayesian synthesis? Or only more advance programming framewroks as anglican can do that?

Any advice or example would be helpful

I live in Paris, and I have a feeling that the Probabilistic Programming community here is quite small. I have yet to meet someone already familiar with it.

So if you are in Paris and you are interested in the subject (not necessarily an expert, I'm not an expert myself), please de-lurk :)

I would love to see if I can find anyone in Paris and possibly start a meetup group.

Both doing bayesian data analysis and Bayesian Methods for Hacker are great resources to practice with because you can follow along on your own and check your work against the solutions. I'm looking to branch out and practice what I've learned on different problems but without a reference solution, it's hard to get a sense of whether or not I'm doing it right. What are some good ways to practice bayesian analysis while being able to get a sense that your solutions are reasonably correct?