Bayesian belief networks: Odds and ends

In artificial 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 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 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.