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.
The Geometry of Bayesian Programming / Dal Lago U.; Hoshino N.. - ELETTRONICO. - 2019-:(2019), pp. 8785663.1-8785663.13. (Intervento presentato al convegno 34th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2019 tenutosi a SFU Harbour Centre in Downtown Vancouver, can nel 2019) [10.1109/LICS.2019.8785663].
The Geometry of Bayesian Programming
Dal Lago U.
;
2019
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
