On Assigning Probabilities to New Hypotheses

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00544189" target="_blank" >RIV/67985556:_____/21:00544189 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0167865521002567" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167865521002567</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.patrec.2021.07.011" target="_blank" >10.1016/j.patrec.2021.07.011</a>

Result language
angličtina
Original language name
On Assigning Probabilities to New Hypotheses
Original language description
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 principlenand 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, andn(c) sequential Monte Carlo estimation. They indicate a strong application potential of the proposed technique. Related interesting open research problems are listed.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/LTC18075" target="_blank" >LTC18075: Distributed rational decision making: cooperation aspects</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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