Probability Bootstrapping: Improving Prediction by Fitting Extensional Models to Knowledgeable but Incoherent Probability Judgments,

Abstract

We propose a simple method for choosing a probability distribution as an approximation to a set of incoherent probability judgments. In two different domains we demonstrate that the resulting (coherent) probability estimates come closer to the observed relative frequencies than do the original incoherent judgments.