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Modular Probabilistic Models via Algebraic Effects
[article]
2022
arXiv
pre-print
Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both
arXiv:2203.04608v3
fatcat:7nesx5lhvrbdtfe266vbqarv4e