0450905 20240103211236.020151201235959.9 84951804385 000371579600071 10.1007/978-3-319-26535-3 7 s. E cav_un_epca*0450904978-3-319-26534-60302-9743623-629ChamSpringer International Publishing2015SabriA. Minimum cross-entropy principle Kullback-Leibler divergence Linear opinion pooling Combining probability distributions cav_un_auth*0263972 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Sečkárová Vladimíra UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/AS/seckarova-0450905.pdf cav_un_auth*0292725 GA13-13502S GA ČR In this work we focus on opinion pooling in the finite group of sources introduced in [Seckarova, 2015]. This approach, heavily exploiting Kullback-Leibler divergence (also known as cross-entropy), allows us to combine sources’ opinions given in probabilistic form, i.e. represented by the probability mass function (pmf). However, this approach assumes that sources are equally reliable with no preferences on, e.g., importance of a particular source. The discussion about the influence of the combination by preferences among sources (represented by weights) and numerical demonstration of the derived theory on an illustrative example form the core of this contribution. cav_un_auth*0322736 22nd International Conference on Neural Information Processing (ICONIP2015) 09.11.2015-12.11.2015 Istanbul TR BC 2016 1 PR RVO:67985556 http://hdl.handle.net/11104/0252663 S 0.329 Q2 Q2 2015 84951804385 SCOPUS 000371579600071 WOS cav_un_epca*0450904 Neural Information Processing 978-3-319-26534-6 0302-9743 623 629 Cham Springer International Publishing 2015 Lecture Notes in Computer Science 9490 Sabri A. 340 Tingwen H. 340 Weng K.L. 340 Qingshan L. 340