Bayesian Decision Analysis :
Smith, Jim Q.,
Bayesian Decision Analysis : Principles and Practice / Jim Q. Smith. - 1 online resource (348 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511779237 (ebook)
Bayesian statistical decision theory
QA279.5 / .S628 2010
519.5/42
Bayesian Decision Analysis : Principles and Practice / Jim Q. Smith. - 1 online resource (348 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511779237 (ebook)
Bayesian statistical decision theory
QA279.5 / .S628 2010
519.5/42