Learning Bayesian networks with hidden variables for user modeling

The goal of the research summarized here is to develop methods for learning Bayesian networks on the basis of empirical data, focusing on issues that are especially important in the context of user modeling. These issues include the treatment of theoretically interpretable hidden variables, ways of learning partial networks and combining them into one single compound network, and ways of taking into account the special properties of datasets acquired through psychological experiments.