0431804 20240103204632.520141021235959.9 84912570469 000393407800075 10.1109/MLSP.2014.6958920 6 s. C cav_un_epca*0431803978-1-4799-3693-9ReimsIEEE2014 distributed estimation state-space models Bayesian estimation 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. http://library.utia.cas.cz/separaty/2014/AS/dedecius-0431804.pdf cav_un_auth*0303543 GP14-06678P GA ČR CZ The paper studies the problem of decentralized distributed estimation of the state-space models from the Bayesian viewpoint. The adopted diffusion strategy, consisting of collective adaptation to new data and combination of posterior estimates, is derived in general model-independent form. Its particular application to the celebrated Kalman filter demonstrates the ease of use, especially when the measurement model is rewritten into the exponential family form and a conjugate prior describes the estimated state. cav_un_auth*0306303 The 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014) 21.09.2014-24.09.2014 Reims FR BB 10000 10100 10103 2015 1 PO http://hdl.handle.net/11104/0237640 1 S RIV/67985556:_____/14:00431804!RIV15-GA0-67985556 152501063 Doplnění UT WOS a Scopus 2014 84912570469 SCOPUS 000393407800075 WOS cav_un_epca*0431803 Proceedings of the 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP2014) 978-1-4799-3693-9 Reims IEEE 2014