0444151 20240103210103.920150609235959.9 ÚTIA AV ČR, v.v.i 2015 28 s. P Research Report 2350 Markov chain approximate parameter estimation Bayesian recursive estimation adaptive systems Kullback-Leibler divergence forgetting cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems 100 Ústav teorie informace a automatizace AV ČR, v. v. i. GA13-13502S GA ČR cav_un_auth*0292725 A high-order Markov chain is a universal model of stochastic relations between discrete-valued variables. The exact estimation of its transition probabilities suers from the curse of dimensionality. It requires an excessive amount of informative observations as well as an extreme memory for storing the corresponding su cient statistic. The paper bypasses this problem by considering a rich subset of Markov-chain models, namely, mixtures of low dimensional Markov chains, possibly with external variables. It uses Bayesian approximate estimation suitable for a subsequent decision making under uncertainty. The proposed recursive (sequential, one-pass) estimator updates a product of Dirichlet probability densities (pds) used as an approximate posterior pd, projects the result back to this class of pds and applies an improved data-dependent stabilised forgetting, which counteracts the dangerous accumulation of approximation errors. 2016 BC 1 RVO:67985556 http://hdl.handle.net/11104/0247113 S 2015 2015 ÚTIA AV ČR, v.v.i