Human experts can make judgments and decisions based on uncertain, and often even conflicting, information. A knowledge-based system that is required to perform at least at a similar level of expertise, should be able to cope with this type of information. For this reason, formalisms for representing uncertainty and algorithms for manipulating uncertain information are important research subjects within the field of Artificial Intelligence. Probability theory is one of the oldest theories dealing with the concept of uncertainty; it is therefore no surprise that the applicability of this mathematical theory as a model for reasoning under uncertainty plays an important role. In this course, we will consider probabilistic models for manipulating uncertain information in knowledge-based systems. More specifically, we will consider the theory underlying the framework of probabilistic networks, and discuss the construction of such networks for real-life applications.