Additive Belief-Network Models

TL;DR

This paper introduces additive belief network models (ABNMs) that simplify probabilistic inference, making it more efficient especially with limited data, and extends inference algorithms to leverage their additive structure.

Contribution

The paper presents the additive belief network model, discusses its approximations, and generalizes inference algorithms to improve efficiency and applicability.

Findings

Additive decomposition simplifies inference in belief networks.

ABNMs are more efficient with scarce data.

Generalized inference algorithms exploit additive structure.

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.