Oftentimes decision makers have several forecasts of an uncertain and operationally relevant random variable. A rich literature now exists which argues that in this situation the decision maker should consider forming a forecast as a weighted average of each of the individual forecasts. In this paper, composite forecasting is discussed in a Bayesian context. The ability of the user to control the impact of the data on his composite weights is illustrated by an example using expert opinion forecasts of U.S. hog prices.