Decision makers are sometimes faced with aggregating advice from multiple advisers without knowing what information is driving each adviser’s opinion. Following Budescu and Yu (2006, 2007), we conducted an experiment in which participants first learned to estimate the probability of a disease based on multiple test results. Next, subjects made the same judgments solely on the basis of probabilities given by multiple advisers who may have only received partial information. Experimental results confirm previous findings that decision makers give extreme estimates when advisers are in agreement and compromise estimates when advisors are in disagreement. Unlike previously proposed models that can only account for extreme or compromise estimates but not both, we develop a new Bayesian model that explains both types of judgments. This model provides a rational explanation of information aggregation by assuming that decision makers use the probability estimates of advisers to infer underlying data before making probability judgments.