Modular Probabilistic Models via Algebraic Effects

TL;DR

This paper introduces a modular, reusable approach to probabilistic programming using algebraic effects in Haskell, enabling flexible composition of models and inference procedures.

Contribution

It presents an embedded DSL based on algebraic effects that makes probabilistic models first-class, modular, and reusable for simulation and inference.

Findings

Models are modular and composable.

Simulation and inference are expressed as program transformations.

The approach enhances reusability and flexibility of probabilistic models.

Abstract

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.