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Method of probabilistic inference from learning data in Bayesian networks

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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.

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Authors and Affiliations

  1. Institute of Applied Systems Analysis of the National Technical University of Ukraine “Kyiv Polytechnical Institute,”, Kyiv, Ukraine

    A. N. Terent’yev & P. I. Bidyuk

Authors
  1. A. N. Terent’yev
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  2. P. I. Bidyuk
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Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 93–99, May–June 2007.

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Terent’yev, A.N., Bidyuk, P.I. Method of probabilistic inference from learning data in Bayesian networks. Cybern Syst Anal 43, 391–396 (2007). https://doi.org/10.1007/s10559-007-0061-7

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  • DOI: https://doi.org/10.1007/s10559-007-0061-7

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