Probabilistic expert systems are graphical networks which support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The book will be of interest to researchers and graduate students in artificial intelligence who desire an understanding of the mathematical and statistical basis of probabilistic expert systems, and to students and research workers in statistics wanting an introduction to this fascinating and rapidly developing field. The careful attention to detail will also make this work as an important reference source for all those involved in the theory and applications of probabilistic expert systems. Robert Cowell is Research Fellow and Computer Manager in the Department of Actuarial Science and Statistics of City University, London. He has been working in the field of probabilistic expert systems for over 10 years, and has published a number of research and tutorial articles in the area. A. Philip Dawid is Professor of Statistics at University College London. He has served as Editor of the Journal of the Royal Statistical Society (Series B) and Biometrika, and is currently President-Elect of the International Society for Bayesian Analysis. He holds the Guy Medal in Bronze of the Royal Statistical Society, and the G.W. Snedecor Award for the Best Publication in Biometry. He has published over 100 articles on statistics and related topics.

This book is a winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature.