*Introduction

*Propositional logic

*Probability calculus

*Bayesian networks

*Building Bayesian networks

*Inference by variable elimination

*Inference by factor elimination

*Inference by conditioning

*Models for graph decomposition

*Most likely instantiations

*The complexity of probabilistic inference

*Compiling Bayesian networks

*Inference with local structure

*Approximate inference by belief propagation

*Approximate inference by stochastic sampling

*Sensitivity analysis

*Learning : the maximum likelihood approach

*Learning : the Bayesian approach

Introduces the formal foundations and practical applications of Bayesian networks.