arxiv:1904.07425

The Geometry of Bayesian Programming

Published on Apr 16, 2019

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Abstract

A geometry of interaction model is presented for a typed lambda-calculus with sampling and soft conditioning, using measurable spaces and partial functions, and validated with both distribution and sampling semantics.

AI-generated summary

We give a geometry of interaction model for a typed lambda-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.

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