Upper bounds for variational stochastic complexities of Bayesian networks

Kazuho Watanabe, Motoki Shiga, Sumio Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In recent years, variational Bayesian learning has been used as an approximation of Bayesian learning. In spite of the computational tractability and good generalization performance in many applications, its statistical properties have yet to be clarified. In this paper, we analyze the statistical property in variational Bayesian learning of Bayesian networks which are widely used in information processing and uncertain artificial intelligence. We derive upper bounds for asymptotic variational stochastic complexities of Bayesian networks. Our result theoretically supports the effectiveness of variational Bayesian learning as an approximation of Bayesian learning.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Pages139-146
Number of pages8
ISBN (Print)3540454853, 9783540454854
DOIs
Publication statusPublished - 2006
Event7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos, Spain
Duration: 2006 Sept 202006 Sept 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
Country/TerritorySpain
CityBurgos
Period06/9/2006/9/23

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