Evidence propagation in credal networks: An exact algorithm based on separately specified sets of probability

José Carlos F. da Rocha, Fabio G. Cozman

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

3 Scopus citations

Abstract

© Springer-Verlag Berlin Heidelberg 2002. Probabilistic models and graph-based independence languages have often been combined in artificial intelligence research. The Bayesian network formalism is probably the best example of this type of association. In this article we focus on graphical structures that associate graphs with sets of probability measures — the result is referred to as a credal network. We describe credal networks and review an algorithm for evidential reasoning that we have recently developed. The algorithm substantially simplifies the computation of upper and lower probabilities by exploiting an independence assumption (strong independence) and a representation based on separately specified sets of probability measures. The algorithm is particularly efficient when applied to polytree structures. We then discuss a strategy for approximate reasoning in multi-connected networks, based on conditioning.
Original languageAmerican English
Title of host publicationEvidence propagation in credal networks: An exact algorithm based on separately specified sets of probability
Pages376-385
Number of pages10
ISBN (Electronic)3540001247, 9783540001249
DOIs
StatePublished - 1 Jan 2002
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)
Volume2507
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 → …

Fingerprint

Dive into the research topics of 'Evidence propagation in credal networks: An exact algorithm based on separately specified sets of probability'. Together they form a unique fingerprint.

Cite this