An Application of Computable Distributions to the Semantics of Probabilistic Programs
Our attempt to give semantics to a core, functional probabilistic programming language uses computable distributions. The semantics uses largely standard techniques from denotational semantics. One nice property of computable distributions is that they are completely characterized by a sampling algorithm. Hence, it is possible to faithfully implement (in a Turing-complete language) a semantics based on computable distributions as a sampling library. We are interested in the pros and cons of using computable distributions as opposed to measure theory in giving semantics to probabilistic programming languages.
Sat 23 JanDisplayed time zone: Guadalajara, Mexico City, Monterrey change
16:30 - 18:00
|eXchangeable Random Primitives|
|An Application of Computable Distributions to the Semantics of Probabilistic Programs|
|On The Semantic Intricacies of Conditioning|
Friedrich Gretz RWTH Aachen University, Nils Jansen RWTH Aachen University, Benjamin Lucien Kaminski RWTH Aachen University, Joost-Pieter Katoen RWTH Aachen University, Federico Olmedo RWTH Aachen UniversityPre-print