Bayesian Decision Analysis : Principles and Practice

Bayesian Decision Analysis Supports Principled Decision Making In Complex Domains. This Textbook Takes The Reader From A Formal Analysis Of Simple Decision Problems To A Careful Analysis Of The Sometimes Very Complex And Data Rich Structures Confronted By Practitioners. The Book Contains Basic Material On Subjective Probability Theory And Multi-attribute Utility Theory, Event And Decision Trees, Bayesian Networks, Influence Diagrams And Causal Bayesian Networks. The Author Demonstrates When And How The Theory Can Be Successfully Applied To A Given Decision Problem, How Data Can Be Sampled And Expert Judgements Elicited To Support This Analysis, And When And How An Effective Bayesian Decision Analysis Can Be Implemented. Evolving From A Third-year Undergraduate Course Taught By The Author Over Many Years, All Of The Material In This Book Will Be Accessible To A Student Who Has Completed Introductory Courses In Probability And Mathematical Statistics--provided By Publisher. Machine Generated Contents Note: Preface; Part I. Foundations Of Decision Modeling: 1. Introduction; 2. Explanations Of Processes And Trees; 3. Utilities And Rewards; 4. Subjective Probability And Its Elicitation; 5. Bayesian Inference For Decision Analysis; Part Ii. Multi-dimensional Decision Modeling: 6. Multiattribute Utility Theory; 7. Bayesian Networks; 8. Graphs, Decisions And Causality; 9. Multidimensional Learning; 10. Conclusions; Bibliography. Jim Q. Smith. Includes Bibliographical References (p. 322-334) And Index. Foundations of decision modeling. Introduction -- Explanations of processes and trees -- Utilities and rewards -- Subjective probability and its elicitation -- Bayesian inference for decision analysis Multi-dimensional decision modeling. Multiattribute utility theory -- Bayesian networks -- Graphs, decisions and causality -- Multidimensional learning -- Conclusions. - "Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics"--Provided by publisher Real-world decisions involve numbers, but they also involve people. Bayesian decision analysis gives a principled framework for reconciling the two. This textbook explains how to use statistical theory, psychology and algorithms to guide decision makers so that they can marshal available evidence and defend their actions.