Výzkumná zpráva

Kárný M., Andrýsek J., Bodini A., Guy T., Kracík J., Nedoma

Research Report-Istituto di Matematica Applicata e Tecnologi

8-10MI

Istituto di Matematica Applicata e Tecnologie Informatiche

Milano

2008

Ing. Miroslav Kárný, DrSc. (ÚTIA - Oddělení adaptivních systémů)

Bayesian estimation, prior knowledge, automatised knowledge

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.