Published June 24, 2019 | Version v1
Conference paper

The Geometry of Bayesian Programming

Description

We give a geometry of interaction model for a typed λ-calculus endowed with operators for sampling from a continuous uniform distribution and soft conditioning, namely a paradigmatic calculus for higher-order Bayesian programming. The model is based on the category of measurable spaces and partial measurable functions, and is proved adequate with respect to both a distribution-based and a sampling-based operational semantics.

Abstract

International audience

Additional details

Created:
December 4, 2022
Modified:
December 1, 2023