The descriptive complexity of Bayesian network specifications

Fabio G. Cozman, Denis D. Mauá

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

2 Scopus citations

Abstract

© Springer International Publishing AG 2017. We adapt the theory of descriptive complexity to Bayesian networks, by investigating how expressive can be specifications based on predicates and quantifiers. We show that Bayesian network specifications that employ first-order quantification capture the complexity class PP; that is, any phenomenon that can be simulated with a polynomial time probabilistic Turing machine can be also modeled by such a network. We also show that, by allowing quantification over predicates, the resulting Bayesian network specifications capture the complexity class PPNP, a result that does not seem to have equivalent in the literature.
Original languageAmerican English
Title of host publicationThe descriptive complexity of Bayesian network specifications
Pages93-103
Number of pages11
ISBN (Electronic)9783319615806
DOIs
StatePublished - 1 Jan 2017
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10369 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

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