0370444 20240103200153.720120109235959.9 000301342800002 10.1080/03610918.2011.598992 8 s. cav_un_epca*02564340361-0918415 (2012)582-589Taylor & Francis Bayesian methods Particle filters Recursive estimation 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*0228606 Hofman Radek Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf VG20102013018 GA MV cav_un_auth*0265869 GA102/08/0567 GA ČR cav_un_auth*0239566 SGS 10/099/OHK3/1T/16 ČVUT CZ CEZ:AV0Z10750506 The authors are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. They propose a linear regression model within Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixture, where the weights of components are tuned with a particle filter. The mixture reflects a priori given hypotheses on different scenarios of (expected) parameters' evolution. 2012 BB 2 http://hdl.handle.net/11104/0204246 STATISTICSPROBABILITY 0.515 0.065 9.1 0.00359 0.332 838 139 0.425 Q3 10.019 4.701 Q4 Q4 2012 000301342800002 WOS cav_un_epca*0256434 Communications in Statistics - Simulation and Computation 0361-0918 1532-4141 Roč. 41 č. 5 2012 582 589 Taylor & Francis