Quantitative Bayesian inference by qualitative knowledge modeling

Rui Chang, Martin Stetter

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

4 Scopus citations

Abstract

In this paper, we present a novel framework for modeling Bayesian networks and performing quantitative Bayesian inference based on qualitative knowledge. Our method transforms qualitative statements into a set of structure and parameter constraints by making use of a proposed qualitative knowledge model. These qualitative constraints are utilized to restrain uncertainties in Bayesian model space and to generate a class of Bayesian networks which are consistent with the qualitative knowledge. Quantitative probabilistic inference is calculated by model averaging with Monte Carlo integration method. The method is benchmarked on ASIA network. Results suggest that our method can reasonably predict quantitative inference from a set of realistic qualitative statements.

Original languageEnglish (US)
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages2563-2568
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: Aug 12 2007Aug 17 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

Conference2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL
Period8/12/078/17/07

ASJC Scopus subject areas

  • Software

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