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On Iteration in Discrete Probabilistic Programming

Authors Mateo Torres-Ruiz, Robin Piedeleu, Alexandra Silva, Fabio Zanasi

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Author Details

Mateo Torres-Ruiz
  • University College London, United Kingdom
Robin Piedeleu
  • University College London, United Kingdom
Alexandra Silva
  • Cornell University, Ithaca, NY, United States of America
Fabio Zanasi
  • University College London, United Kingdom
  • University of Bologna, OLAS team (INRIA), Italy

Funding

This work was partially supported by ERC grant Autoprobe (no. 101002697; Piedeleu, Silva, and Torres-Ruiz).
  • Zanasi, Fabio: Fabio Zanasi acknowledges support from EPSRC EP/V002376/1 and MIUR P2022HXNSC (PRIN 2022 PNRR - Next Generation EU).

Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.

Cite As Get BibTex

Mateo Torres-Ruiz, Robin Piedeleu, Alexandra Silva, and Fabio Zanasi. On Iteration in Discrete Probabilistic Programming. In 9th International Conference on Formal Structures for Computation and Deduction (FSCD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 299, pp. 20:1-20:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.FSCD.2024.20

Abstract

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Program semantics
Keywords
  • Probabilistic programming
  • Programming languages semantics
  • Unbounded iteration

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