0544189 20240103230042.020210801235959.9 000694711500021 85111504429 10.1016/j.patrec.2021.07.011 6 s. P cav_un_epca*02573890167-86551501 (2021)170-175Elsevier minimum relative-entropy principle prior probability hypothesis cav_un_auth*0101124 Kárný Miroslav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS 100 A Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/AS/karny-0544189.pdf https://www.sciencedirect.com/science/article/pii/S0167865521002567 LTC18075 GA MŠk CZ cav_un_auth*0372050 CA16228 The European Cooperation in Science and Technology (COST) XE cav_un_auth*0372051 The paper proposes the way how to assign a proper prior probability to a new, generally compound, hypothesis. To this purpose, it uses the minimum relative-entropy principle and a forecaster-based knowledge transfer. Methodologically, it opens a way towards enriching the standard Bayesian framework by the possibility to extend the set of models during learning without the need to restart. The presented use scenarios concern: (a) creating new hypotheses, (b) learning problems with an insuffcient amount of data, and (c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed. WOS IN 10000 10200 10201 2022 1 4 A sml 4as 20231122145841.9 RVO:67985556 http://hdl.handle.net/11104/0321363 S ELSEVIER Publishing Agreement 20210726 3+4 Article Computer Science Artificial Intelligence C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 4.253 1.041 8.4 0.01281 0.804 18442 188 1.479 33.16 5.74 6461 Q1 4.502 364 Q2 63.8 Q3 Q2 0.91 Q2 63.793 2021 Soubory v repozitáři: karny-0544189-PATREC8308.html 85111504429 SCOPUS PUBMED 000694711500021 WOS cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 150 č. 1 2021 170 175 Elsevier