Semantics of higher-order probabilistic programs with conditioning
Logic in Computer Science
2019-03-01 v1 Machine Learning
Programming Languages
Abstract
We present a denotational semantics for higher-order probabilistic programs in terms of linear operators between Banach spaces. Our semantics is rooted in the classical theory of Banach spaces and their tensor products, but bears similarities with the well-known Scott semantics of higher-order programs through the use ordered Banach spaces which allow definitions in terms of fixed points. Being based on a monoidal rather than cartesian closed structure, our semantics effectively treats randomness as a resource.
Cite
@article{arxiv.1902.11189,
title = {Semantics of higher-order probabilistic programs with conditioning},
author = {Fredrik Dahlqvist and Dexter Kozen},
journal= {arXiv preprint arXiv:1902.11189},
year = {2019}
}
Comments
17 pages, proofs in the Appendix