# [Monad-Bayes](https://monad-bayes.netlify.app/) A library for probabilistic programming in Haskell. <!-- [](https://hackage.haskell.org/package/monad-bayes) [](http://stackage.org/lts/package/monad-bayes) [](http://packdeps.haskellers.com/reverse/monad-bayes) [](https://buildkite.com/tweag-1/monad-bayes) --> [See the docs](https://monad-bayes.netlify.app/) for a user guide, notebook-style tutorials, an example gallery, and a detailed account of the implementation. <!-- Monad-Bayes is a library for **probabilistic programming in Haskell**. The emphasis is on composition of inference algorithms, and is implemented in terms of monad transformers. --> <!-- See the [documentation](https://monad-bayes.netlify.app/) for a quick-start user guide and a reference overview of how it all works. --> Created by [Adam Scibior][adam-web] ([@adscib][adam-github]), documentation, website and newer features by [Reuben][reuben-web], maintained by [Tweag][tweagio]. ## Project status 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. ## Background 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][haskell2015-tag]. [1] Adam M. Ścibior, Zoubin Ghahramani, and Andrew D. Gordon. 2015. [Practical probabilistic programming with monads][haskell2015-doi]. 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][icfp2018-doi]. 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-doi]. Thesis. University of Cambridge. ## Hacking 1. Install `stack` by following [these instructions][stack-install]. 2. 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: 1. Compile the source: `stack build` 2. If you do not have `nix` [install it](https://nixos.org/download.html). 3. Run `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. 4. Run `jupyter-lab` from the nix shell to load the notebooks. Your mileage may vary. [adam-github]: https://github.com/adscib [adam-web]: https://www.cs.ubc.ca/~ascibior/ [reuben-web]: https://reubencohngordon.com/ [haskell2015-doi]: https://doi.org/10.1145/2804302.2804317 [haskell2015-tag]: https://github.com/tweag/monad-bayes/tree/haskell2015 [icfp2018-doi]: https://doi.org/10.1145/3236778 [models]: https://github.com/tweag/monad-bayes/tree/master/models [stack-install]: https://docs.haskellstack.org/en/stable/install_and_upgrade/ [thesis-doi]: https://doi.org/10.17863/CAM.42233 [tweagio]: https://tweag.io