# [Monad-Bayes](https://monad-bayes-site.netlify.app/_site/about.html)

A library for probabilistic programming in Haskell. 

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[See the website](https://monad-bayes-site.netlify.app/_site/about.html) for an overview of the documentation, library, tutorials, examples (and a link to this very source code). 

<!-- 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. -->

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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