0443931 20240103210046.320150609235959.9 84946039806 000427402903064 10.1109/ICASSP.2015.7178563 5 s. E cav_un_epca*0444627978-1-4673-6997-81520-61493207-3211PiscatawayIEEE Computer Society2015 Bayesian filtration diffusion distributed filtration cav_un_auth*0242543 Dedecius Kamil UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0306051 Djurić P. M. US http://library.utia.cas.cz/separaty/2015/AS/dedecius-0443931.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ Distributed filtration of state-space models with sensor networks assumes knowledge of a model of the data-generating process. However, this assumption is often violated in practice, as the conditions vary from node to node and are usually only partially known. In addition, the model may generally be too complicated, computationally demanding or even completely intractable. In this contribution, we propose a distributed filtration framework based on the novel approximate Bayesian computation (ABC) methods, which is able to overcome these issues. In particular, we focus on filtration in diffusion networks, where neighboring nodes share their observations and posterior distributions. cav_un_auth*0316738 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015) 19.05.2015-24.05.2015 Brisbane AU BB 10000 10100 10103 2016 2 PO RVO:67985556 http://hdl.handle.net/11104/0247110 1 S RIV/67985556:_____/15:00443931!RIV16-AV0-67985556 191684179 Doplnění UT WOS RIV/67985556:_____/15:00443931!RIV16-GA0-67985556 191719270 Doplnění UT WOS n.a. Proceedings Paper Acoustics|Engineering Electrical Electronic 2015 84946039806 SCOPUS 000427402903064 WOS cav_un_epca*0444627 Proceedings of 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 978-1-4673-6997-8 1520-6149 3207 3211 Piscataway IEEE Computer Society 2015