PSense: Automatic Sensitivity Analysis for Probabilistic Programs

Zixin Huang, Zhenbang Wang, Sasa Misailovic

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

Abstract

PSense is a novel system for sensitivity analysis of probabilistic programs. It computes the impact that a noise in the values of the parameters of the prior distributions and the data have on the program’s result. PSense relates the program executions with and without noise using a developer-provided sensitivity metric. PSense calculates the impact as a set of symbolic functions of each noise variable and supports various non-linear sensitivity metrics. Our evaluation on 66 programs from the literature and five common sensitivity metrics demonstrates the effectiveness of PSense.

Original languageEnglish (US)
Title of host publicationAutomated Technology for Verification and Analysis - 16th International Symposium, ATVA 2018, Proceedings
EditorsChao Wang, Shuvendu K. Lahiri
PublisherSpringer-Verlag Berlin Heidelberg
Pages387-403
Number of pages17
ISBN (Print)9783030010898
DOIs
StatePublished - Jan 1 2018
Event16th International Symposium on Automated Technology for Verification and Analysis, ATVA 2018 - Los Angeles, United States
Duration: Oct 7 2018Oct 10 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11138 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Symposium on Automated Technology for Verification and Analysis, ATVA 2018
CountryUnited States
CityLos Angeles
Period10/7/1810/10/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Huang, Z., Wang, Z., & Misailovic, S. (2018). PSense: Automatic Sensitivity Analysis for Probabilistic Programs. In C. Wang, & S. K. Lahiri (Eds.), Automated Technology for Verification and Analysis - 16th International Symposium, ATVA 2018, Proceedings (pp. 387-403). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11138 LNCS). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-030-01090-4_23