Programming and Markov Categories.
- An Introduction to Probabilistic Programming (van de Meent, Paige, Yang, Wood, 2021)
- Kleisli Semantics for Conditioning in Probabilistic Programming (Cho, Jacobs, 2022)
- Probabilistic Programming Semantics for Name Generation (Stein et al)
- A Synthetic Approach to Markov Kernels (Fritz, 2020)
- A Presentation of the Category of Stochastic Matrices (Fritz)
- Dilations and information flow axioms in categorical probability (Fritz, Gonda, Houghton-Larsen, Perrone, Stein, 2022)
- Markov Categories and Information Theory (Perrone, 2022)
Formalization.
- Verified Density Compilation for a Probabilistic Programming Language (Tassarotti, Tristan, 2024)
- Coquelicot, A User-Friendly Library of Real Analysis for Coq (Boldo, Lelay, Melquiond, 2015)
Languages, grants, and projects.
- Probabilistic Programming for Advancing Machine Learning (PPAML) (DARPA)
- Facebook Probability and Programming Research Awards (Facebook)
- Microsoft Infer.NET
- Pyro, Deep Universal Probabilistic Programming (Uber Labs, Bingham et al)
- ERC BLAST grant (Staton)
- Stan, A Probabilistic Programming Language
- Hakaru
- Church language
- Anglican language
- Unsure calculator, the very naive motivation for probabilistic programming.
Literature
See also.