0431479 20240103204610.020140912235959.9 000342159700009 84907191888 10.1109/LSP.2014.2353652 5 s. E cav_un_epca*02532121070-9908222 (2015)197-201Institute of Electrical and Electronics Engineers distributed estimation mixture models bayesian inference cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0306030 Reichl Jan Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0306051 Djurić P. M. US http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431479.pdf GP14-06678P GA ČR CZ cav_un_auth*0303543 The letter studies the problem of sequential estimation of mixtures in diffusion networks whose nodes communicate only with their adjacent neighbors. The adopted quasi-Bayesian approach yields a probabilistically consistent and computationally non-intensive and fast method, applicable to a wide class of mixture models with unknown component parameters and weights. Moreover, if conjugate priors are used for inferring the component parameters, the solution attains a closed analytic form. 2017 BB 3 4 A 4a 20231122140419.8 RVO:67985556 http://hdl.handle.net/11104/0236075 S ENGINEERING.ELECTRICAL&ELECTRONIC 1.852 0.449 6.3 0.01575 0.862 5098 0.822 Q1 1.432 483 Q2 73.247 63.2 Q2 Q2 63.23 2015 Soubory v repozitáři: dedecius-0431479.pdf 84907191888 SCOPUS 000342159700009 WOS cav_un_epca*0253212 IEEE Signal Processing Letters 1070-9908 1558-2361 Roč. 22 č. 2 2015 197 201 Institute of Electrical and Electronics Engineers