“This book is a classic.... The strengths of this text are twofold. First, it gives a general and well-motivated introduction to the principles of Bayesian decision theory that should be accessible to anyone with a good mathematical statistics background. Second, it provides a good introduction to Bayesian inference in general with particular emphasis on the use of subjective information to choose prior distributions.”

— Mark J. Schervish
Journal of the American Statistical Association

Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. Besides developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.

Thus, in a self-contained comprehensive way, the book shows that the Bayesian approach in statistics – integration of statistics with decision making in areas such as management, engineering, public policy and clinical medicine, is operational and relevant for real-world decision making under uncertainty.