Bayesian networks : a practical guide to applications / edited by Olivier Pourret, Patrick Naim, Bruce Marcot.

Includes bibliographical references (pages 385-425) and index.

Medical diagnosis -- Clinical decision support -- Complex genetic models -- Crime risk factor analysis -- Spatial dynamics in France -- Inference problems in forensic science -- Conservation of marbled murrelets in British Columbia -- Classifiers for modeling of mineral potential -- Student modeling -- Sensor validation -- An information retrieval system -- Reliability analysis of systems -- Terrorism risk management -- Credit-rating of companies -- Classification of Chilean wines -- Pavement and bridge management -- Complex industrial process operation -- Probability of default for large corporates -- Risk management in robotics -- Enhancing human cognition.

"This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering." "Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks."--Jacket.

Print version record.