Fully probabilistic knowledge expression and incorporation

0438275 - ÚTIA 2015 RIV US eng J - Článek v odborném periodiku
Kárný, Miroslav - Guy, Tatiana Valentine - Kracík, J. - Nedoma, Petr - Bodini, A. - Ruggeri, F.
Fully probabilistic knowledge expression and incorporation.
Statistics and its Interface. Roč. 7, č. 4 (2014), s. 503-515. ISSN 1938-7989. E-ISSN 1938-7997
Grant CEP: GA ČR GA13-13502S
Institucionální podpora: RVO:67985556
Klíčová slova: Bayesian estimation * knowledge elicitation * just-in-time modelling * controlled autoregressive model
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
Impakt faktor: 2.933, rok: 2014
http://library.utia.cas.cz/separaty/2014/AS/karny-0438275.pdf

An exploitation of prior knowledge in parameter estimation becomes vital whenever measured data is not informative enough. Elicitation of quantified prior knowledge is a well-elaborated art in societal and medical applications but not in the engineering ones. Frequently required involvement of a facilitator is mostly unrealistic due to either facilitator’s high costs or complexity of modelled relationships that cannot be grasped by humans. This paper provides a facilitator-free approach based on an advanced knowledgesharing methodology. It presents the approach on commonly available types of knowledge and applies the methodology to a normal controlled autoregressive model.
Trvalý link: http://hdl.handle.net/11104/0242028