0462059 20240103212531.020160831235959.9 85006052659 000391891900289 10.1109/EUSIPCO.2016.7760500 5 s. C cav_un_epca*0462058978-0-9928-6266-41508-1512BudapestEUSIPCO2016 parameter estimation Bayes theory mixture models cav_un_auth*0306051 Djurić P. M. US 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. http://library.utia.cas.cz/separaty/2016/AS/dedecius-0462059.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ We address the problem of sequential parameter estimation over networks using the Bayesian methodology. Each node sequentially acquires independent observations, where all the observations in the network contain signal(s) with unknown parameters. The nodes aim at obtaining accurate estimates of the unknown parameters and to that end, they collaborate with their neighbors. They communicate to the neighbors their latest posterior distributions of the unknown parameters. The nodes fuse the received information by using mixtures with weights proportional to the predictive distributions obtained from the respective node posteriors. Then they update the fused posterior using the next acquired observation, and the process repeats. We demonstrate the performance of the proposed approach with computer simulations and confirm its validity cav_un_auth*0332877 24th European Signal Processing Conference (EUSIPCO) 29.08.2016-02.09.2016 Budapest HU BB 2017 2 PR RVO:67985556 http://hdl.handle.net/11104/0261904 1 S n.a. Proceedings Paper Engineering Electrical Electronic 2016 85006052659 SCOPUS 000391891900289 WOS cav_un_epca*0462058 Proc. 2016 24th European Signal Processing Conference (EUSIPCO) 978-0-9928-6266-4 1508 1512 Budapest EUSIPCO 2016