Abstract
AbstractThis paper examines how a Bayesian decision maker might update her distributions for continuous variablesXi, 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, we assume that the decision maker transforms the experts' quantiles in terms of her own prior distribution for eachXi.A model using such a transformation is presented and its properties are examined.
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