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

0445817 - ÚTIA 2016 RIV BG eng J - Journal Article
Sečkárová, Vladimíra
DYNAMIC PARAMETER ESTIMATION BASED ON MINIMUM CROSS-ENTROPY METHOD FOR COMBINING INFORMATION SOURCES.
Pliska Studia Mathematica Bulgarica. Roč. 24, č. 5 (2015), s. 181-188. ISSN 0204-9805.
[XVI-th International Summer Conference on Probability and Statistics (ISCPS-2014). Pomorie, 21.6.-29.6.2014]
R&D Projects: GA ČR GA13-13502S
Grant - others:GA UK(CZ) SVV 260225/2015
Institutional support: RVO:67985556
Keywords : minimum cross-entropy principle * Kullback-Leibler divergence * dynamic diffusion estimation
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2015/AS/seckarova-0445817.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0247988