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Decision theory provides a formal framework for making logical choices in the face of uncertainty. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. This book presents an overview of the fundamental concepts and outcomes of rational decision making under uncertainty, highlighting the implications for statistical practice.


The authors have developed a series of self contained chapters focusing on bridging the gaps between the different fields that have contributed to rational decision making and presenting ideas in a unified framework and notation while respecting and highlighting the different and sometimes conflicting perspectives.


This book:

* Provides a rich collection of techniques and procedures.
* Discusses the foundational aspects and modern day practice.
* Links foundations to practical applications in biostatistics, computer science, engineering and economics.
* Presents different perspectives and controversies to encourage readers to form their own opinion of decision making and statistics.


Decision Theory is fundamental to all scientific disciplines, including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book.

Review

“Also anyone interested in learning more about decision theoretic experimental design (a topic of growing interest for example in sequential clinical trials) will find a useful overview and a good starting point for further investigations.”  (Stat Papers, 2011)

 

"Decision theory is fundamental to all scientific disciplines., including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book." (Mathematical Reviews, 2011)

From the Inside Flap

Decision theory provides a formal framework for making logical choices in the face of uncertainty. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. This book presents an overview of the fundamental concepts and outcomes of rational decision making under uncertainty, highlighting the implications for statistical practice.


The authors have developed a series of self contained chapters focusing on bridging the gaps between the different fields that have contributed to rational decision making and presenting ideas in a unified framework and notation while respecting and highlighting the different and sometimes conflicting perspectives.


This book:

* Provides a rich collection of techniques and procedures.
* Discusses the foundational aspects and modern day practice.
* Links foundations to practical applications in biostatistics, computer science, engineering and economics.
* Presents different perspectives and controversies to encourage readers to form their own opinion of decision making and statistics.


Decision Theory is fundamental to all scientific disciplines, including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book.

From the Back Cover

Decision theory provides a formal framework for making logical choices in the face of uncertainty. Given a set of alternatives, a set of consequences, and a correspondence between those sets, decision theory offers conceptually simple procedures for choice. This book presents an overview of the fundamental concepts and outcomes of rational decision making under uncertainty, highlighting the implications for statistical practice.


The authors have developed a series of self contained chapters focusing on bridging the gaps between the different fields that have contributed to rational decision making and presenting ideas in a unified framework and notation while respecting and highlighting the different and sometimes conflicting perspectives.


This book:

* Provides a rich collection of techniques and procedures.
* Discusses the foundational aspects and modern day practice.
* Links foundations to practical applications in biostatistics, computer science, engineering and economics.
* Presents different perspectives and controversies to encourage readers to form their own opinion of decision making and statistics.


Decision Theory is fundamental to all scientific disciplines, including biostatistics, computer science, economics and engineering. Anyone interested in the whys and wherefores of statistical science will find much to enjoy in this book.

About the Author

Giovanni Parmigiani is the author of Decision Theory: Principles and Approaches, published by Wiley.

Lurdes Yoshiko Tani Inoue is a Brazilian-born statistician of Japanese descent, who specializes in Bayesian inference. She works as a professor of biostatistics in the University of Washington School of Public Health.

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5 out of 55 out of 5
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Top reviews from the United States

Michael R. Chernick
5.0 out of 5 stars
Decision Theory with a Bayesian orientation
Reviewed in the United States on February 25, 2010
There have been many books on decision theory going all the way back to Wald in the 1940s. We have the texts by Blackwell and Girshick, Luce and Raiffa, Raiffa and Schlaifer, DeGroot, Berger, Wald, Ferguson, and Chernoff and Moses to name some of the classics. Parmigiani... See more
There have been many books on decision theory going all the way back to Wald in the 1940s. We have the texts by Blackwell and Girshick, Luce and Raiffa, Raiffa and Schlaifer, DeGroot, Berger, Wald, Ferguson, and Chernoff and Moses to name some of the classics. Parmigiani and Inoue provide an up-to-date acount on the subject. New ultiity fuinctions such as QALY (quality of life years) are introduced and the chapters 2-6 provide the foundations of the Bayesian approach to decision theory. Then chapters 7-11 provide the theory from the Bayesian viewpoint. They include the concept of admissibility and Stein''s famous shrinkage estimators that show the maximum likelihood estimator for a multivariate mean vector is not admissible (3 or more dimensions).

The third section titled Optimal Design deals with multistage decision problems and introduces dynamic programming as a technique to solve some of these problems.
12 people found this helpful
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Richard Hahn
5.0 out of 5 stars
Great decision theory textbook
Reviewed in the United States on September 20, 2010
This textbook covers all of the basics in decision theory. It is clear and thorough. One especially nice thing about this book is that it collects several results in one place that aren''t covered in much detail in many statistics texts: the Stein phenomenon, complete... See more
This textbook covers all of the basics in decision theory. It is clear and thorough. One especially nice thing about this book is that it collects several results in one place that aren''t covered in much detail in many statistics texts: the Stein phenomenon, complete class theorems, and state-dependent utilities, to name a few. I''d pair this with Schervish''s Theory of Statistics and Gelman and Hill''s Data Analysis Using Regression and Multilevel/Hierarchical Models for a three-pack covering the core of Bayesian statistics -- decision theory, statistical theory and applied regression modeling.
15 people found this helpful
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Kindle Customer
5.0 out of 5 starsVerified Purchase
Well written and useful resource.
Reviewed in the United Kingdom on July 13, 2021
Brilliant, useful and informative.
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Decision high quality Theory: discount Principles and Approaches outlet online sale

Decision high quality Theory: discount Principles and Approaches outlet online sale

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