A library for probabilistic programming in Haskell using probability monads. The emphasis is on composition of inference algorithms implemented in terms of monad transformers.
Created by Adam Scibior (@adscib), maintained by Tweag I/O.
Now that monad-bayes has been released on Hackage, we will focus on improving
documentation. In the meantime, see the models folder for examples.
The basis for the code in this repository is the ICFP 2018 paper [2]. For the
code associated with the Haskell2015 paper [1], see the haskell2015
tag.
[1] Adam M. Ścibior, Zoubin Ghahramani, and Andrew D. Gordon. 2015. Practical probabilistic programming with monads. In Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell (Haskell ’15), Association for Computing Machinery, Vancouver, BC, Canada, 165–176.
[2] Adam M. Ścibior, Ohad Kammar, and Zoubin Ghahramani. 2018. Functional programming for modular Bayesian inference. In Proceedings of the ACM on Programming Languages Volume 2, ICFP (July 2018), 83:1–83:29.
[3] Adam M. Ścibior. 2019. Formally justified and modular Bayesian inference for probabilistic programs. Thesis. University of Cambridge.
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Install
stackby following these instructions. -
Clone the repository using one of these URLs:
git clone [email protected]:tweag/monad-bayes.git git clone https://github.com/tweag/monad-bayes.git
Now you can use stack build, stack test and stack ghci.