0349891 20240103194143.920101129235959.9 Praha ČVUT v Praze 2010 93 s. Bayesian modelling estimation theory 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. http://library.utia.cas.cz/separaty/2010/AS/dedecius-0349891.pdf 1M0572 GA MŠk cav_un_auth*0001814 GA102/08/0567 GA ČR cav_un_auth*0239566 CEZ:AV0Z10750506 In the thesis, a new method called `partial forgetting' is developed. Its purpose is to solve the main drawbacks of most forgetting techniques. In comparison to most of them, it is defined in the Bayesian framework as a general method, theoretically independent of the underlaying parametric model and practically directly usable for a wide class of models. It is specified for one popular member of that class - the Gaussian (auto)regressive model with external disturbances. Though the mathematics related to it is nontrivial, the derivation was done almost analytically and only a minor need of numerical approximation of the digamma function appeared. By formulation of hypotheses about the multivariate parameter entries, the method allows to track them independently and to forget them with different rates. It possesses stabilizing property, which, in the Gaussian autoregressive model, helps to prevent the parameter covariance blow-up phenomenon, when the gain of the estimation algorithm grows without bounds for nonexciting signals. 2011 BB 19.11.2010 Ph.D. Ústav aplikované matematiky, Fakulta dopravní ČVUT v Praze Na Florenci 25, 110 00 Praha 1 2010 http://hdl.handle.net/11104/0190019 2010 2010 Praha ČVUT v Praze