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Programming language: Jupyter Notebook
License: MIT License
Tags: Statistics    
Latest version: v0.1.1.0

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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.

Project status

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.


  1. Install stack by following these instructions.

  2. 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.