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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.
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 . 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 firstname.lastname@example.org:tweag/monad-bayes.git git clone https://github.com/tweag/monad-bayes.git
Now you can use
stack test and