  In order to improve forecasts, a decisionmaker often combines probabilities
given by various sources, such as human experts and machine learning
classifiers. When few training data are available, aggregation can be improved
by incorporating prior knowledge about the event being forecasted and about
salient properties of the experts. To this end, we develop a generative
Bayesian aggregation model for probabilistic classi cation. The model includes
an event-specific prior, measures of individual experts' bias, calibration,
accuracy, and a measure of dependence betweeen experts. Rather than require
absolute measures, we show that aggregation may be expressed in terms of
relative accuracy between experts. The model results in a weighted logarithmic
opinion pool (LogOps) that satis es consistency criteria such as the external
Bayesian property. We derive analytic solutions for independent and for
exchangeable experts. Empirical tests demonstrate the model's use, comparing
its accuracy with other aggregation methods.
