0447119 20240103210535.820150910235959.9 000363348400013 84943770986 10.1016/j.ins.2015.07.038 14 s. P cav_un_epca*02567520020-02553261 (2016)188-201Elsevier 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. http://library.utia.cas.cz/separaty/2015/AS/karny-0447119.pdf 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 suffers from the curse of dimensionality. It requires an excessive amount of informative observations as well as an extreme memory for storing the corresponding sufficient 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 4 A hod 4ah 20231122141128.6 RVO:67985556 http://hdl.handle.net/11104/0249079 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, INFORMATION SYSTEMS S 2 Article Computer Science Information Systems COMPUTERSCIENCE.INFORMATIONSYSTEMS 4.732 1.041 4.5 0.04473 1.09 23222 1.781 Q1 3.784 801 Q1 80.37 95.5 Q1 Q1 95.548 2016 Soubory v repozitáři: karny-0447119.pdf 84943770986 SCOPUS 000363348400013 WOS cav_un_epca*0256752 Information Sciences 0020-0255 1872-6291 Roč. 326 č. 1 2016 188 201 Elsevier