Bayesian Belief Networks: Odds and Ends
Linda C. van der Gaag · The Computer Journal · 1996
In artifcial intelligence research, the 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 ecient 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 paper provides a tutorial introduction to the belief network framework and highlights some issues of ongoing research in applying the framework for real-life problem solving. 1 Introduction Over the past few decades interest in the results of arti cial intelligence research has been growing to an increasing extent. Especially the area of knowledge-based systems has attracted much attention. The phrase knowledge-based system, or expert system, ...