Statistics and Its Interface
Volume 7 (2014)
Special Issue on Modern Bayesian Statistics (Part I)
Guest Editor: Ming-Hui Chen (University of Connecticut)
Fully probabilistic knowledge expression and incorporation
Pages: 503 – 515
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 knowledge-sharing methodology. It presents the approach on commonly available types of knowledge and applies the methodology to a normal controlled autoregressive model.
Bayesian estimation, automatised knowledge elicitation, just-in-time modelling, controlled autoregressive model
2010 Mathematics Subject Classification
Primary 62C10, 62F15. Secondary 62M10.