2026-07-22T22:18:06Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20349 2025-02-10T15:26:41Z ddc:004 open_access

On Iteration in Discrete Probabilistic Programming Torres-Ruiz, Mateo Piedeleu, Robin Silva, Alexandra Zanasi, Fabio Probabilistic programming Programming languages semantics Unbounded iteration Discrete probabilistic programming languages provide an expressive tool for representing and reasoning about probabilistic models. These languages typically define the semantics of a program through its posterior distribution, obtained through exact inference techniques. While the semantics of standard programming constructs in this context is well understood, there is a gap in extending these languages with tools to reason about the asymptotic behaviour of programs. In this paper, we introduce unbounded iteration in the context of a discrete probabilistic programming language, give it a semantics, and show how to compute it exactly. This allows us to express the stationary distribution of a probabilistic function while preserving the efficiency of exact inference techniques. We discuss the advantages and limitations of our approach, showcasing their practical utility by considering examples where bounded iteration poses a challenge due to the inherent difficulty of assessing the proximity of a distribution to its stationary point. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Mateo Torres-Ruiz and Robin Piedeleu and Alexandra Silva and Fabio Zanasi 2024 Is Part Of LIPIcs, Volume 299, 9th International Conference on Formal Structures for Computation and Deduction (FSCD 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FSCD.2024.20 urn:nbn:de:0030-drops-203490 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSCD.2024.20 eng https://creativecommons.org/licenses/by/4.0/legalcode