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Journal Article

DYNAMIC PARAMETER ESTIMATION BASED ON MINIMUM CROSS-ENTROPY METHOD FOR COMBINING INFORMATION SOURCES

Sečkárová Vladimíra

: Pliska Studia Mathematica Bulgarica vol.24, 5 (2015), p. 181-188

: XVI-th International Summer Conference on Probability and Statistics (ISCPS-2014), (Pomorie, BG, 21.6.-29.6.2014)

: SVV 260225/2015, GA UK, GA13-13502S, GA ČR

: minimum cross-entropy principle, Kullback-Leibler divergence, dynamic diffusion estimation

: http://library.utia.cas.cz/separaty/2015/AS/seckarova-0445817.pdf

(eng): When combining information sources, e.g. measuring devices or experts, we deal with two problems: which combining method to choose (linear combination, geometric mean) and how to measure the reliability of the sources, i.e. how to assign the weights to them. We introduce a method which overcomes such shortcomings. Proposed method, based on minimization of the Kullback-Leibler divergence with specific constraints, directly combines data, i.e. probability vectors, thus no additional step to obtain the weights is needed. The detailed description of the proposed method and a comparison with recently introduced dynamic diffusion estimation, which heavily depends on the determination of the weights, form the core of this contribution.

: BB

07.01.2019 - 08:39