We show that the distinction between risk and uncertainty can bemodeled within a Bayesian framework if (1) an agent faces repeated draws from an unknown distribution and (2) his utility is not separable across these draws. The resulting uncertainty premium can help explain a number of asset-pricing anomalies. To better model uncertainty, we dene a new distribution called the categorical Dirichlet which can account for similarity of states within a category.