In previous work we have proposed Hierarchical Bayesian Networks (HBNs) as an extension of Bayesian Networks. HBNs are able to deal with structured domains, and use knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) aggregations of simpler nodes. Every aggregate node is itself an HBN modelling independencies inside a subset of the whole world under consideration. In this paper we introduce inference in HBNs using a stochastic sampling algorithm, and a learning method for HBNs based on the Cooper and Herskovits structure likelihood measure. We furthermore explore how HBNs can be applied to the problem of modelling right arm motion in cello playing. This problem is inherently hierarchical and therefore well-suited for modelling by HBNs. The task is to construct a descriptive model for a player's movements observing the position of different joints as well as muscular activity of the right arm during the execution of a short musical extract. %We demonstrate how the learning algorithm we propose efficiently %constructs a model for the given data. Different datasets were used to construct models both for an amateur and a professional cello player, and differences between the derived HBNs can be used to interpret the differences on each person's ``tacit knowledge" on the task.