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Combining Marginal Probability Distributions via Minimization of Weighted Sum of Kullback-Leibler Divergences
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SYSNO ASEP 0359399 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Combining Marginal Probability Distributions via Minimization of Weighted Sum of Kullback-Leibler Divergences Author(s) Kracík, Jan (UTIA-B) Source Title International Journal of Approximate Reasoning. - : Elsevier - ISSN 0888-613X
Roč. 52, č. 6 (2011), s. 659-671Number of pages 13 s. Language eng - English Country US - United States Keywords combining probabilities ; Kullback-Leibler divergence ; maximum likelihood ; expert opinions ; linear opinion pool Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA102/08/0567 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000291137100001 EID SCOPUS 79955569018 DOI 10.1016/j.ijar.2011.01.002 Annotation The paper deals with the problem of combining marginal probability distributions as a means for aggregating pieces of expert information. The combined distribution is searched as a minimizer of a weighted sum of Kullback–Leibler divergences of the given marginal distributions and corresponding marginals of the searched one. Necessary and sufficient conditions for a distribution to be a minimizer are stated. For discrete random variables an iterative algorithm for approximate solution of the minimization problem is proposed and its convergence is proved. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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