Method of probabilistic inference from learning data in Bayesian networks

Authors:
A. N. Terent'Yev;P. I. Bidyuk
Affiliations:
Institute of Applied Systems Analysis of the National Technical University of Ukraine "Kyiv Polytechnical Institute,", Kyiv, Ukraine;Institute of Applied Systems Analysis of the National Technical University of Ukraine "Kyiv Polytechnical Institute,", Kyiv, Ukraine
Venue:
Cybernetics and Systems Analysis
Year:
2007

Citing 2
Cited 0

Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning
A comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer architectures for computing marginals of probability distributions

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

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Abstract

Bayesian networks (BN) are a powerful tool for various data-mining systems. The available methods of probabilistic inference from learning data have shortcomings such as high computation complexity and cumulative error. This is due to a partial loss of information in transition from empiric information to conditional probability tables. The paper presents a new simple and exact algorithm for probabilistic inference in BN from learning data.