Fully probabilistic knowledge expression and incorporation

0315659 - ÚTIA 2009 IT eng V - Research Report
Kárný, Miroslav - Andrýsek, Josef - Bodini, A. - Guy, Tatiana Valentine - Kracík, Jan - Nedoma, Petr - Ruggeri, F.
Fully probabilistic knowledge expression and incorporation.
Milano: Istituto di Matematica Applicata e Tecnologie Informatiche, 2008. 28 s. Research Report-Istituto di Matematica Applicata e Tecnologie Informatiche, 8-10MI.
R&D Projects: GA MŠMT 1M0572; GA MŠMT 2C06001; GA ČR GA102/08/0567
Institutional research plan: CEZ:AV0Z10750506
Keywords : Bayesian estimation * prior knowledge * automatised knowledge elicitation
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2008/AS/karny-fully probabilistic knowledge expression and incorporation.pdf

Exploitation of prior knowledge in parameter estimation is vital whenever data is not informative enough. Elicitation and quantification of prior knowledge is a well-elaborated art in social and medical appliations but not in engineering ones. Frequently required involvment of a facilitator is mostly unrealistic due to either facilitators' high costs or the high complexitu of modelled relationships that cannot be grasped by the human. This paper provides a facilitator-free approach exploiting a methodology of knowledge sharing. The considered task assumes prospective models be indexed by an unknown finite-dimensional parameter. The parameter is estimated using (i) observed data; (ii) a prior probability density function (pdf); and (iii) uncertain expert's information on the modelled data. The parametric model specifies pdf of the system's output conditioned on realised data and parameter.
Permanent Link: http://hdl.handle.net/11104/0165800