Modular Probabilistic Models via Algebraic Effects

Fuente: arXiv
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Main Authors: Nguyen, Minh, Perera, Roly, Wang, Meng, Wu, Nicolas
Format: Preprint
Published: 2022
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author Nguyen, Minh
Perera, Roly
Wang, Meng
Wu, Nicolas
author_facet Nguyen, Minh
Perera, Roly
Wang, Meng
Wu, Nicolas
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2203_04608
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Modular Probabilistic Models via Algebraic Effects
Nguyen, Minh
Perera, Roly
Wang, Meng
Wu, Nicolas
Programming Languages
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.
title Modular Probabilistic Models via Algebraic Effects
topic Programming Languages
url https://arxiv.org/abs/2203.04608