0410587 20240103182224.720060210235959.9 8 s. cav_un_epca*02562180005-1098376 (2001)1071-1078Elsevier Markov chain clustering Bayesian mixture estimation cav_un_auth*0101219 Valečková Markéta UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Kárný Miroslav UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0212452 Sutanto E. L. GB 09I 1999/12058 IST XE GA102/99/1564 GA ČR cav_un_auth*0004444 AV0Z1075907 Markov chains are black box models ideal for describing stochastic digitised systems. Although the identification of their parameters can be a relatively easy task to perform, the dimensionality involved become undesirable large. This significant drawback can be overcome by exploiting smoothness of the underlying system. The paper present a novel hybrid off-line algorithm to locate areas which merit detailed model description. It comprises Bayesian parameter estimation and Mean tracking algorithm. BC AS http://hdl.handle.net/11104/0130676 UTIA-B 20010056 2001 cav_un_epca*0256218 Automatica 0005-1098 1873-2836 Roč. 37 č. 6 2001 1071 1078 Elsevier