Learning probabilistic networks

A probabilistic network is a graphical model that encodes probabilistic rel ationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influen ces between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the paramete rs of a model. We then progress to showing how the structure of the model c an be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as poss ible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.