0477044 20240103214400.120170815235959.9 85023175924 000407100700002 10.1109/TSP.2017.2725226 11 s. P cav_un_epca*02567271053-587X6519 (2017)5153-5163 Diffusion network Diffusion estimation Heterogeneous parameters Multitask networks cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0263972 Sečkárová Vladimíra Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477044.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ cav_un_auth*0331019 GA16-09848S GA ČR Collaborative estimation of partially common parameters over ad hoc diffusion networks where the nodes directly communicate with their neighbors is a challenging task. The problem complexity is significantly high under the lack of knowledge which parameters are shared and among which network nodes. In this paper, we propose an adaptive framework suitable for this task. It is abstractly formulated in the Bayesian and information-theoretic paradigms and, therefore, versatile and easily applicable to a relatively wide class of models. If the observation models belong to the exponential family and the same functional types of prior probability distributions are used for estimation of the shared parameters, the method reduces to an analytically tractable variational algorithm extended by a procedure that passes messages among network nodes. A simulation example demonstrates that the collaboration improves estimation performance of both the shared and strictly local parameters, compared with the noncollaborative scenario. BD 10000 10100 10102 2018 2 4 A hod 4ah 20231122142603.3 RVO:67985556 http://hdl.handle.net/11104/0274027 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, CYBERNETICS S 3+4 Article Engineering Electrical Electronic 3+4 Article Engineering Electrical Electronic 3+4 Article Engineering Electrical Electronic ENGINEERING.ELECTRICAL&ELECTRONIC 4.705 1.024 8.2 0.05557 1.559 35782 1.247 3.637 462 Q1 87.621 87.9 Q1 Q1* 1.76 Q1 87.885 2017 Soubory v repozitáři: dedecius-0477044.pdf 85023175924 SCOPUS PUBMED 000407100700002 WOS cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 65 č. 19 2017 5153 5163