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  • Siddharth Bhat

    Siddharth Bhat - Monad-bayes: Probabilistic programming in haskell, tutorial & implementation

    Siddharth Bhat
    Siddharth Bhat
    IIIT Hyderabad
    schedule 1 year ago
    Sold Out!
    20 Mins
    Case Study

    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:

    Paper on which the talk is based on:

1. What got you into Functional Programming (FP)?

I was a C/C++ person who used to dabble with graphics and games. I eventually learnt rust, where I found building a game to be a terrible experience, due to the inability to mutate to my heart's desire. This led a friend to tell me about the existence of something mysterious called "functional reactive programming". I googled, found Haskell, thought to myself "I suppose learning this new language will take a week" --- I haven't left since. 

2. What has been your best moment or highlight working with FP?

The moment when I began to study denotational semantics formally, and thought to myself, "hey, I _know_ all of this from hanging out on the haskell IRC!". It was a mind-blowing experience, to realise that I had picked up so much of theoretical computer science "for free" from studying this neat language.