Combining experts’ opinions using a Normal-Wishart model

This paper examines how a Bayesian decision maker might update his/her distributions for continuous variables Xi, i=1,2,...,upon hearing experts’ forecasts expressed as quantiles. To utilize the relationship between the decision maker and experts, and to avoid problems associated with different scales and ranges of the variables, it is assumed that the decision maker transforms the experts’ quantiles in terms of his/her own prior distribution for each Xi. A model using such a transformation is presented and its properties are examined.