Additive belief-network models

Authors:
Paul Dagum;Adam Galper
Affiliations:
Section on Medical Informatics, Stanford University School of Medicine and Rockwell PMo Alto Laboratory, Palo Alto, California;Medical Informatics, Stanford University School of Medicine, Stanford, California
Venue:
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Year:
1993

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Abstract

The inherent intractability of probabilistic inference has hindered the application of belief networks to large domains. Noisy OR-gates [30] and probabilistic similarity networks [18, 17] escape the complexity of inference by restricting model expressiveness. Recent work in the application of belief-network models to time-series analysis and forecasting [9, 10] has given rise to the additive belief-network model (ABNM). We (1) discuss the nature and implications of the approximations made by an additive decomposition of a belief network, (2) show greater efficiency in the induction of additive models when available data are scarce, (3) generalize probabilistic inference algorithms to exploit the additive decomposition of ABNMs, (4) show greater efficiency of inference, and (5) compare results on inference with a simple additive belief network.