KSL-93-01
## Additive Belief-Network Models

**Reference: **
Dagum, P. &
Galper, A. Additive Belief-Network Models. Washington, D.C, 1993.

**Abstract:**
The intractability of available probabilistic inference
algorithms hinders belief network applications to large
domains. Researchers have shown that both exact and
approximate probabilistic inference is NP-hard, and
therefore, we do not hope to find tractable solutions to
inference in large applications. The intractability of
inference, known implicitly to designers of large
applications, and the formal proofs of its complexity that
came afterwards, together motivated alternative research
directions in hopes of tractable solutions to the impasse.
From this work arose, for example, noisy OR-gates used in
QMR-DT and probabilistic similarity networks.
Motivated by recent developments in belief network models for
time-series analysis and forecasting, we define "additive
belief network models" (ABNM). We (1) discuss the nature and
implications of the approximations made by an additive
decomposition of a belief network, (2) prove greater
efficiency in the induction of additive models when available
data is scarce, (3) generalize the Lauritzen-Spiegelhalter
inference algorithm to exploit the additive decomposition of
ABNMs (4) prove greater efficiency of inference, and (5)
present implementation results on induction and on inference
of belief networks.

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