Probabilistic Networks and Expert Systems

WINNER OF THE 2001 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. 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 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.

• Bayesian Network
• Graphical model
• Junction tree
• Machine learning
• Probability propagation
• artificial intelligence
• expert system
• intelligence
• learning
• modeling
• probabilistic network
• statistics
• uncertainty

From the reviews:

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

"This important book fills a void in the graphical Markov models literature. The authors have summarized their extensive and influential work in this area and provided a valuable resource both for educators and for practitioners."

• School of Mathematics, Actuarial Science, and Statistics, City University, London, London, UK

  Robert G. Cowell

• Department of Statistical Science, University College London, London, UK

  A. Philip Dawid

• Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark

  Steffen L. Lauritzen

• MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK

  David J. Spiegelhalter

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