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On Assigning Probabilities to New Hypotheses
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SYSNO ASEP 0544189 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title On Assigning Probabilities to New Hypotheses Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID Number of authors 1 Source Title Pattern Recognition Letters. - : Elsevier - ISSN 0167-8655
Roč. 150, č. 1 (2021), s. 170-175Number of pages 6 s. Publication form Print - P Language eng - English Country NL - Netherlands Keywords minimum relative-entropy principle ; prior probability ; hypothesis Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects LTC18075 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000694711500021 EID SCOPUS 85111504429 DOI 10.1016/j.patrec.2021.07.011 Annotation 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.Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022 Electronic address https://www.sciencedirect.com/science/article/pii/S0167865521002567
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