0379258 20240103201107.420120828235959.9 000309943200050 10.1109/SSP.2012.6319658 4 s. P cav_un_epca*0379257978-1-4673-0182-4197-200Ann ArborIEEE2012 Marginalized particle filter Rao-Blackwellization maximum entropy cav_un_auth*0101207 Šmídl Václav 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*0264146 Gustafsson F. SE http://library.utia.cas.cz/separaty/2012/AS/Smidl-bayesian estimation of forgetting factor in adaptive filtering and change detection.pdf GAP102/11/0437 GA ČR CZ cav_un_auth*0273082 An adaptive filter is derived in a Bayesian framework from the assumption that the difference in the parameter distribution from one time to another is bounded in terms of the Kullback-Leibler divergence. We show an explicit link to the general concepts of exponential forgetting, and outline the details for a linear Gaussian model with unknown parameter and covariance. We extend the problem to an unknown forgetting factor, where we provide a particular prior that allows for abrupt changes in forgetting, which is useful in change detection problems. The Rao-Blackwellized particle filter is used for the implementation, and its performance is assessed in a simulation of system with abrupt changes of parameters. cav_un_auth*0282796 2012 IEEE Statistical Signal Processing Workshop Ann Arbor 05.08.2012-08.08.2012 US 2013 BD PO RVO:67985556 http://hdl.handle.net/11104/0210510 2012 000309943200050 WOS cav_un_epca*0379257 Proceedings of the IEEE Statistical Signal Processing Workshop 2012 978-1-4673-0182-4 197 200 Ann Arbor IEEE 2012