Bayesian inference is crucial to challenging scenarios that involve complex probabilistic models, which are usually intractable. In this work, we develop an expectation propagation approach to learn finite mixture models of EDCMs. The EDCM (Elkan 2006) is an exponential-family approximation to the widely used Dirichlet Compound Multinomial distribution and has been shown to offer excellent modeling capabilities in the case of sparse count data. Expectation propagation is a deterministic approach that provides accurate approximations to the full posterior and allows to include prior beliefs in the model as opposed to the maximum-likelihood method, which provides point estimates only. We evaluate the efficiency of our framework on several datasets for sentiment analysis and shape recognition.