This was one of the predecessor books to decision analysis, written by Howard Raiffa's collaborator at Harvard.

Probability and Statistics for Business Decisions

Science & Math

Schlaifer, Robert

Mathematics

McGraw=Hill

The Bayesian revolution in statistics—where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine—is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty. Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems. Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After 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.

Introduction to Statistical Decision Theory

Business & Money

Raiffa, Howard

Pratt, John W.

Economics

Mit Pr

The Bayesian revolution in statistics―where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine―is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty.
Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems.
Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After 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.

Literature & Fiction

PRATT, JOHN

Dramas & Plays

The MIT Press

Applied Statistical Decision Theory

"In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems." ―From the foreword to their classic work Applied Statistical Decision Theory. First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley

Wiley-Interscience

Optimal Statistical DecisionsThis book provides a thorough discussion in the theory and methodology of optimal statistical decisions. The volume represents a landmark. It still remains the clearest introduction to Bayesian statistical decision theory even in, this, a paperback reprint of the original book dating back to 1970 (under the aegis of McGraw Hill, Inc.). While the content does not include the computational advances that have become so popular and well used today, neither does it neglect what purposes are served by those computations. DeGroot’s book, with its clear exposition of Bayesian principles, is useful to keep those purposes in mind. Of particular note is the fact that Bayesian and sequential decision problems are explained from the bottom-up with great care and clarity. Bayesian Inference in Statistical AnalysisIts main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach. Applied Statistical Decision Theory"In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems."—From the foreword to their classic work Applied Statistical Decision Theory. First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley

Optimal Statistical Decision & Bayesian Inference in Statistical Analysis & Applied Statistical Decision Theory

DeGroot, Morris H.

Box, George E. P.

Tiao, George C.

wiley

Development of aircraft engines

Maxwell Reprint Co

"This book, the first in a group entitled Studies in Managerial Economics, is addressed to persons who are interested in using statistics as a tool in practical problems of decision making under conditions of uncertainty and who also have the necessary training in mathematics and statistics to employ these analytical techniques".

Harvard University Press

Computer Programs for Elementary Decision Analysis (Studies in Managerial Economics)

Computers & Technology

Analysis of Decisions Under Uncertainty

Management & Leadership

Krieger Pub. Co

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