Probabilistic Reasoning ( Probabilistisch Redeneren ) authors

Preface In artificial-intelligence research, the probabilistic-network, or (Bayesian) belief-network framework for automated reasoning with uncertainty is rapidly gaining in popularity. The framework provides a powerful formalism for representing a joint probability distribution on a set of statistical variables. In addition, it offers algorithms for efficient probabilistic inference. At present, more and more knowledge-based systems employing the framework are being developed for various domains of application, ranging from probabilistic information retrieval to medical diagnosis. This syllabus provides a tuto-rial introduction to the probabilistic-network framework and highlight some issues of ongoing research in applying the framework for problem solving in real-life domains. Each chapter includes a number of exercises, and answers or hints to some of them (indicated by a *) are provided at the end. This syllabus was first written in the late 1990s by L.C. van der Gaag and has been continuously under development eversince. Since 2001, adaptions and extensions have been made mostly by S. Renooij. The syllabus is by no means devoid from imperfections and any useful comments on its contents are greatly appreciated by the authors. For the 2006 edition, several references to relevant recent research have been added to Chapters 4, 5, and 6. In addition, material on the subject of sensitivity analysis has been extended.