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 simulation and inference. We also
demonstrate how simulation and inference can be expressed naturally as
composable program transformations using algebraic effect handlers
