An Application of Computable Distributions to the Semantics of Probabilistic Programs
Abstract
In this chapter, we explore how (Type-2) computable distributions can be used to give both (algorithmic) sampling and distributional semantics to probabilistic programs with continuous distributions. Towards this end, we sketch an encoding of computable distributions in a fragment of Haskell and show how topological domains can be used to model the resulting PCF-like language. We also examine the implications that a (Type-2) computable semantics has for implementing conditioning. We hope to draw out the connection between an approach based on (Type-2) computability and ordinary programming throughout the chapter as well as highlight the relation with constructive mathematics (via realizability).
Keywords
Cite
@article{arxiv.1806.07966,
title = {An Application of Computable Distributions to the Semantics of Probabilistic Programs},
author = {Daniel Huang and Greg Morrisett and Bas Spitters},
journal= {arXiv preprint arXiv:1806.07966},
year = {2020}
}
Comments
Accepted as contribution to "Foundations of Probabilistic Programming"