Additive Belief-Network Models

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Additive Belief-Network Models

Bibtype  techreport

Has publishing details  1993

Has title  Additive Belief-Network Models

Has where published  KSL-93-01

Has year  1993

Title  Additive Belief-Network Models

Year  1993

Abstract  The intractability of available probabilis 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. ction and on inference of belief networks.

Address  Washington, D.C +

Author  Paul Dagum and Adam Galper +

Has author  Paul Dagum and Adam Galper +

Has identifier  Additive Belief-Network Models +

Institution  Knowledge Systems, AI Laboratory +

Ksl tr id  Additive Belief-Network Models +

Number  Additive Belief-Network Models +

Process note  YES +

Categories  KSL Technical Report +, Publication +, Technical Report +

 

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