0445817 20240103210257.320150723235959.9 8 s. P cav_un_epca*03939880204-9805245 (2015)181-188 minimum cross-entropy principle Kullback-Leibler divergence dynamic diffusion estimation cav_un_auth*0263972 Sečkárová Vladimíra Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/AS/seckarova-0445817.pdf SVV 260225/2015 GA UK CZ cav_un_auth*0318139 GA13-13502S GA ČR cav_un_auth*0292725 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. cav_un_auth*0318138 XVI-th International Summer Conference on Probability and Statistics (ISCPS-2014) Pomorie 21.6.-29.6.2014 BG 2016 BB 1 RVO:67985556 http://hdl.handle.net/11104/0247988 cav_un_auth*0296001 MFF UK Univerzita Karlova v Praze, Matematicko-fyzikální fakulta CZ S 2015 cav_un_epca*0393988 Pliska Studia Mathematica Bulgarica 0204-9805 Roč. 24 č. 5 2015 181 188