The Geometry of Bayesian Programming - Archive ouverte HAL
Communication Dans Un Congrès Année : 2019

The Geometry of Bayesian Programming

Résumé

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.
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Dates et versions

hal-02399343 , version 1 (07-01-2020)

Identifiants

Citer

Ugo Dal Lago, Naohiko Hoshino. The Geometry of Bayesian Programming. LICS 2019 - 34th Annual ACM/IEEE Symposium on Logic in Computer Science, Jun 2019, Vancouver, Canada. pp.1-13, ⟨10.1109/LICS.2019.8785663⟩. ⟨hal-02399343⟩
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