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Novel algorithm for Bayesian network parameter learning with informative prior constraints

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

Abstract

The generalization performance of a learned Bayesian network largely depends on the quality of the prior provided to the learning machine. Indeed, the prior distribution is designed to provide additive domain expert knowledge to the parameters in a Bayesian network which tolerate some variance around these initial counts. The learning task is combinatorial regulates on this initial counts by the data statistics. The use of a prior distribution becomes even more critical in case of scarce data.

Original languageEnglish (US)
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424469178
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: Jul 18 2010Jul 23 2010

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Country/TerritorySpain
CityBarcelona
Period7/18/107/23/10

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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