Additive Belief-Network Models
This work addresses the problem of scaling belief networks for large domains, offering incremental improvements in efficiency for time-series analysis and forecasting applications.
The paper tackles the intractability of probabilistic inference in belief networks by introducing additive belief network models (ABNMs), showing greater efficiency in model induction with scarce data and improved inference speed compared to existing methods.
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.