Calibrating and Combining Precipitation Probability Forecasts

The thorniest part of the Bayesian combination procedure is the assessment of a likelihood function by the decision maker to represent his beliefs regarding the quality of the information and the nature of the dependence among the sources.

Abstract

Imagine a decision maker who has heard from one or more information sources regarding the probability of some future event and who desires to use this information to revise his personal beliefs concerning the event. One approach to this problem involves the decision maker treating the probabilities as data in a Bayesian inferential problem, the output of which is an updated probability regarding the event in question. The thorniest part of the Bayesian combination procedure is the assessment of a likelihood function by the decision maker to represent his beliefs regarding the quality of the information and, in the case of multiple sources, the nature of the dependence among the sources.