On Assigning Probabilities to New Hypotheses

0544189 - ÚTIA 2022 RIV NL eng J - Journal Article
Kárný, Miroslav
On Assigning Probabilities to New Hypotheses.
Pattern Recognition Letters. Roč. 150, č. 1 (2021), s. 170-175. ISSN 0167-8655. E-ISSN 1872-7344
R&D Projects: GA MŠMT(CZ) LTC18075
Grant - others:The European Cooperation in Science and Technology (COST)(XE) CA16228
Institutional support: RVO:67985556
Keywords : minimum relative-entropy principle * prior probability * hypothesis
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 4.757, year: 2021
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2021/AS/karny-0544189.pdf https://www.sciencedirect.com/science/article/pii/S0167865521002567

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.
Permanent Link: http://hdl.handle.net/11104/0321363