Department of Pattern Recognition

(eng): 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.