Modular probabilistic models via algebraic effects

Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict a particular instance of simulation inference, limiting their reusability. In other PPLs, are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic modular, first-class, reusable for both inference. We also demonstrate how can be expressed naturally composable program transformations using effect handlers.