Continualization of Probabilistic Programs With Correction

Jacob Laurel, Sasa Misailovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Probabilistic Programming offers a concise way to represent stochastic models and perform automated statistical inference. However, many real-world models have discrete or hybrid discrete-continuous distributions, for which existing tools may suffer non-trivial limitations. Inference and parameter estimation can be exceedingly slow for these models because many inference algorithms compute results faster (or exclusively) when the distributions being inferred are continuous. To address this discrepancy, this paper presents Leios. Leios is the first approach for systematically approximating arbitrary probabilistic programs that have discrete, or hybrid discrete-continuous random variables. The approximate programs have all their variables fully continualized. We show that once we have the fully continuous approximate program, we can perform inference and parameter estimation faster by exploiting the existing support that many languages offer for continuous distributions. Furthermore, we show that the estimates obtained when performing inference and parameter estimation on the continuous approximation are still comparably close to both the true parameter values and the estimates obtained when performing inference on the original model.

Original languageEnglish (US)
Title of host publicationProgramming Languages and Systems- 29th European Symposium on Programming, ESOP 2020 held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Proceedings
EditorsPeter Müller
PublisherSpringer
Pages366-393
Number of pages28
ISBN (Print)9783030449131
DOIs
StatePublished - 2020
Event29th European Symposium on Programming, ESOP 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020 - Dublin, Ireland
Duration: Apr 25 2020Apr 30 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th European Symposium on Programming, ESOP 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020
Country/TerritoryIreland
CityDublin
Period4/25/204/30/20

Keywords

  • Continuity
  • Parameter Synthesis
  • Probabilistic Programming
  • Program Approximation
  • Program Transformation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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