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A library for probabilistic programming in Haskell.
See the website 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. -->
<!-- See the documentation for a quick-start user guide and a reference overview of how it all works. -->
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 . For the
code associated with the Haskell2015 paper , see the
 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.
 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.
 Adam M. Ścibior. 2019. Formally justified and modular Bayesian inference for probabilistic programs. Thesis. University of Cambridge.
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 test and
To view the notebooks, go to the website. To use the notebooks interactively:
- Compile the source:
- If you do not have
nix develop --system x86_64-darwin --extra-experimental-features nix-command --extra-experimental-features flakes- this should open a nix shell. For Linux use
jupyter-labfrom the nix shell to load the notebooks.
Your mileage may vary.