We take a dive into probabilistic programming, beginning with a high-level explanation of what probabilistic programming is. We then continue to see how to use the monad-bayes library for performing tasks such as regression in a bayesian formalism. We also look into the implementation of the library, which involves performing an interesting sampling method (markov-chain-monte-carlo) on a very unique space (the space of computational traces of the program). We finally end with next-steps for the haskell community to improve in the probablistic programming space, ala pyro for python
Here is our tiny re-implementation of monad-bayes, boiled down to the essentials: https://github.com/bollu/shakuni
Paper on which the talk is based on: http://mlg.eng.cam.ac.uk/pub/pdf/SciGhaGor15.pdf