Additive Belief-Network Models

The inherent intractability of probabilistic inference has hindered the application of belief networks to large domains. Noisy ORgates [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 beliefnetwork 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.