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Fully probabilistic knowledge expression and incorporation
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SYSNO ASEP 0438275 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Fully probabilistic knowledge expression and incorporation Author(s) Kárný, Miroslav (UTIA-B) RID, ORCID
Guy, Tatiana Valentine (UTIA-B) RID, ORCID
Kracík, J. (CZ)
Nedoma, Petr (UTIA-B)
Bodini, A. (IT)
Ruggeri, F. (IT)Number of authors 6 Source Title Statistics and its Interface - ISSN 1938-7989
Roč. 7, č. 4 (2014), s. 503-515Number of pages 13 s. Publication form Print - P Language eng - English Country US - United States Keywords Bayesian estimation ; knowledge elicitation ; just-in-time modelling ; controlled autoregressive model Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA13-13502S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000348624200008 EID SCOPUS 84920065538 DOI 10.4310/SII.2014.v7.n4.a7 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2015
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