0461646 20240103212459.820160811235959.9 84987915625 000390840200071 10.1109/SSP.2016.7551775 5 s. E cav_un_epca*0461645978-1-4673-7802-4362-366Palma de Mallorca, ŠpanělskoIEEE2016 diffusion estimation distributed estimation exponential family cav_un_auth*0242543 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Dedecius Kamil UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0263972 Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Sečkárová Vladimíra UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/AS/dedecius-0461646.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ cav_un_auth*0292725 GA13-13502S GA ČR The state-of-art methods for distributed estimation of mixtures assume the existence of a common mixture model. In many practical situations, this assumption may be too restrictive, as a subset of parameters may be purely local, e.g., if the numbers of observable components differ across the network. To reflect this issue, we propose a new online Bayesian method for simultaneous estimation of local parameters, and diffusion estimation of global parameters. The algorithm consists of two steps. First, the nodes perform local estimation from own observations by means of factorized prior/posterior distributions. Second, a diffusion optimization step is used to merge the nodes' global parameters estimates. A simulation example demonstrates improved performance in estimation of both parameters sets. cav_un_auth*0332142 2016 IEEE Statistical Signal Processing Workshop 26.06.2016-29.06.2016 Palma de Mallorca ES BD 2017 2 PO RVO:67985556 http://hdl.handle.net/11104/0261345 S n.a. Proceedings Paper Engineering Electrical Electronic|Telecommunications 2016 84987915625 SCOPUS 000390840200071 WOS cav_un_epca*0461645 Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing 978-1-4673-7802-4 362 366 Palma de Mallorca, Španělsko IEEE 2016