(eng): Compositional models, as an alternative to Bayesian networks, are assembled from a system of low-dimensional distributions. Thus, the respective apparatus falls fully into probability theory. The present paper surveys the results supporting the design of computational procedures, without which the application of these models to practical problems would be impossible. The methods of inference cannot do without a possibility to focus on a part of the considered multidimensional model and to incorporate data describing the actual situation. Thus, the paper shows how to compute marginals and conditionals of multidimensional models. Also, the paper briefly solves the problem of computing the effect of an intervention, in case the model is interpreted as a causal model.