0503809 20241106135724.820190408235959.9 85064231311 000469010600001 10.14736/kyb-2019-1-0001 11 s. P cav_un_epca*02971630023-5954551 (2019)1-11Ústav teorie informace a automatizace AV ČR, v. v. i. Bayesian estimation global maximum model structure cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/AS/karny-0503809.pdf https://www.kybernetika.cz/content/2019/1/1 cav_un_auth*0331019 GA16-09848S GA ČR The paper presents the stopping rule for random search for Bayesian model-structure estimation by maximising the likelihood function. The inspected maximisation uses random restarts to cope with local maxima in discrete space. The stopping rule, suitable for any maximisation of this type, exploits the probability of finding global maximum implied by the number of local maxima already found. It stops the search when this probability crosses a given threshold. The inspected case represents an important example of the search in a huge space of hypotheses so common in artificial intelligence, machine learning and computer science. WOS BC 10000 10100 10103 2020 4 A sml 4as 20241106135724.8 RVO:67985556 http://hdl.handle.net/11104/0295600 S Licence Agreement 20190227 2 Article Biochemistry Molecular Biology|Chemistry Multidisciplinary A COMPUTERSCIENCE.CYBERNETICS 0.608 0.12 12.3 0.00084 0.215 778 43 0.241 27.97 0.81 146 Q3 0.582 50 Q4 15.751 6.8 Q4 Q4 0.16 Q4 6.818 2019 Soubory v repozitáři: karny - kybernetika 2019 vol. 55 n.1.pdf 85064231311 SCOPUS PUBMED 000469010600001 WOS cav_un_epca*0297163 Kybernetika 0023-5954 Roč. 55 č. 1 2019 1 11 Ústav teorie informace a automatizace AV ČR, v. v. i.