DocumentCode
1378102
Title
Approximating Bayesian belief networks by arc removal
Author
Van Engelen, Robert A.
Author_Institution
Dept. of Comput. Sci., Leiden Univ., Netherlands
Volume
19
Issue
8
fYear
1997
fDate
8/1/1997 12:00:00 AM
Firstpage
916
Lastpage
920
Abstract
I propose a general framework for approximating Bayesian belief networks through model simplification by arc removal. Given an upper bound on the absolute error allowed on the prior and posterior probability distributions of the approximated network, a subset of arcs is removed, thereby speeding up probabilistic inference
Keywords
directed graphs; inference mechanisms; probability; uncertainty handling; Bayesian belief networks; absolute error; arc removal; model simplification; posterior probability distribution; prior probability distribution; probabilistic inference; Application software; Bayesian methods; Computational modeling; Decision making; Frequency estimation; Information theory; Medical diagnosis; Probability distribution; Uncertainty; Upper bound;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.608295
Filename
608295
Link To Document