A library for probabilistic programming in Haskell.
See the docs for a user guide, notebook-style tutorials, an example gallery, and a detailed account of the implementation.
Created by Adam Scibior (@adscib), documentation, website and newer features by Reuben, maintained by Tweag.
Now that monad-bayes
has been released on Hackage, and the documentation and the API has been updated, we will focus on adding new features. See the Github issues to get a sense of what is being prepared, and please feel free to make requests.
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.
Install stack
by following these instructions.
Clone the repository using one of these URLs:
git clone git@github.com:tweag/monad-bayes.git
git clone https://github.com/tweag/monad-bayes.git
Now you can use stack build
, stack test
and stack ghci
.
To view the notebooks, go to the website. To use the notebooks interactively:
stack build
nix
install it.nix develop --system x86_64-darwin --extra-experimental-features nix-command --extra-experimental-features flakes
- this should open a nix shell. For Linux use x86_64-linux
for --system
option instead. jupyter-lab
from the nix shell to load the notebooks.Your mileage may vary.