F1.2 Inference Methods

This section investigates graphical modeling as a powerful framework for drawing inferences under imprecision and uncertainty. We survey the semantical background and relevant properties of relational, probabilistic, and possibilistic networks and consider evidence propagation in such networks as well as methods for learning them from data. Whereas the probabilistic Bayesian networks and Markov networks are well-known for a couple of years, we focus on possibilistic networks as a promising approach to the efficient treatment of information-compressed uncertain and imprecise knowledge. Keywords possibility theory, knowledge representation, graphical models, relational networks, probabilistic networks, possibilistic networks, evidence propagation, learning from data F1.2.1 Introduction The essential feature of inference is that a certain type of knowledge --- knowledge about truth, probability, (degree of) possibility etc. --- is transferred from given propositions, events, states et..