Abstract Weighted model counting (WMC) on a propositional knowledge base is an effective and general approach to probabilistic inference in a variety of formalisms, inclu ing Bayesian and Markov Networks. However, an inherent limitation of WMC is that it only admits the inference of discrete probability distributions. In this pa per, we introduce a strict generalization of WMC called weighted model integration that is based on annotating Boolean and arithmetic constraints, and combinations thereof. This methodology is shown to capture discrete, continuous and hybrid Markov networks. We then consider the task of parameter learning for a fragment of th language. An empirical evaluation demonstrates the applicability and promise of the proposal.