Applied statistical decision theory应用统计决策论 mobi 下载 网盘 caj lrf pdf txt 阿里云

"In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new ***ytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic ***ysis 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

Foreword

Preface and Introduction

Part I: Experimentation and Decision: General Theory

　1. The Problem and the Two Basic Modes of Analysis

　　1. Description of the Decision Problem

　　　1: The basic data; 2: Assessment of probability measures; 3: Example;

　　　4: The general decision problem as a game.

　　2. Analysis in Extensive Form

　　　1: Backwards induction; 2: Examplc.

　　3. Analysis in Normal Form

　　　1: Decision rules; 2: Performance, error, and utility characteristics; 3:Ex-ample; 4: Equivalence of the extensive and normal form; 5: Bayesian deci- sion theory as a completion of classical theory; 6: Informal choice of a decision rule.

　　4. Combination of Formal and Informal Analysis

　　　1: Unknown costs; cutting the decision tree; 2: Incomplete ***ysis of the decision tree; 3: Example.

　　5. Prior Weights and C***istent Behavior

　2. Sufficient Statistics and Noninformative Stopping

　　1. Introduction

　　　1: Simplifying assumpti***; 2: Bayes' theorem; kernels

　　2. Sufficiency

　　　1: Bayesian definition of sufficiency; 2: Identification of sufficient statistics;

　　 3: Equivalence of the Bayesian and classical definiti*** of sufficiency; 4: Nuisance parameters and marginal sufficiency.

　　3. Noninformative Stopping

　　 1: Data-generating processes and stopping processes; 2: Likelihood of a sample; 3: Noninformative stopping processes; 4: Contrast between the Bayesian and classical treatments of stopping; 5: Summary.

　3. Conjugate Prior Distributi***

　　1. Introduction; Assumpti*** and Definiti***

　　 1: Desiderata for a family of prior distributi***; 2: Sufficient statistics of fixed dimensionality.

2. Conjugate Prior Distributi***

　　 1: Use of the sample kernel as a prior kernel; 2: The posterior distribution when the prior distribution is natural-conjugate; 3: Extension of the domain of the parameter; 4: Extension by introduction of a new parameter; 5: Con- spectus of natural-conjugate densities.

3. Choice and Interpretation of a Prior Distribution

　　 1: Distributi*** fitted to historical relative frequencies; 2: Distributi*** fitted to subjective betting odds; 3: Comparison of the weights of prior and sample evidence; 4: "Quantity of information" and "vague" opini***;

　　 5: Sensitivity ***ysis; 6: Scientific reporting.

4. Analysis in Extensive Form when the Prior Distribution and Sample Likelihood are Conjugate

　　 1: Definiti*** of terminal and preposterior ***ysis; 2: Terminal ***ysis;

　　 3: Preposterior ***ysis.

Part II: Extensive-Form Analysis When Sampling and Terminal Utilities Are Additive

　4. Additive Utility, Opportunity Loss, and the Value of Information:Introduction to Part II

　　1. Basic Assumpti***

2. Applicability of Additive Utilities

　　3. Computation of Expected Utility

　　4. Opportunity Loss

　　 1: Definition of opportunity loss; 2: Extensive-form ***ysis using oppor-tunity loss instead of utility; 3: Opportunity loss when terminal and sam- piing utilities are additive; 4: Direct assessment of terminal opportunity losses; 5: Upper bounds on optimal sample size.

　　5. The Value of Information

　　　l: The value of perfect information; 2: The value of sample information and the net gain of sampling; 3: Summary of relati*** among utilities, op- portunity losses, and value of information.

　5A. Linear Terminal Analysis

　　1. Introduction

　　　1: The transformed state description co; 2: Terminal ***ysis.

　　2. Expected Value of Perfect Information when w is Scalar

　　　1: Two-action problems; 2: Finite-action problems; 3: Evaluation of linear- loss integrals; 4: Examples.

　　3. Preposterior Analysis

　　　1: The posterior mean as a random variable; 2: The expected value of sam- ple information.

　　4. The Prior Distribution of the Posterior Mean for Given e

　　　1: Mean and variance of ~"; 2: Limiting behavior of the distribution; 3　Limiting behavior of integrals when is scalar; 4: Exact distributi*** of;　5: Aouroximati*** to the distribution of : 6: Examt)les.

　　……

part III:Distribution Theory

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书籍介绍

This volume presents Raiffa and Schlaifer's groundbreaking examination of the applicati*** of Bayesian statistical theory to real-life business problems of decision under conditi*** of uncertainty. The emphasis is on identifying a course of action that is logically c***istent with the decision maker's own preference for c***equences, as expressed by numerical utilities, and with the weight s/he attaches to unknown variables, as expressed by numerical probabilities. Well-***anized with self-contained chapters, suitable for both complete and selected study of topics. Clear outline format and detailed table of contents allows for easy referencing. Extremely useful for its historical value. From the reviews: . "Raiffa and Schlaifer have contributed a majo***ork and a major stimulus to furthe***ork. I hope especially that mathematical statisticians will read it and be encouraged to develop the fascinating lines of research that are suggested by it. I recommend it strongly to all statisticians seriously interested in applying statistics to real problems."--ASA Journal. "There is no comparable book. It should have an important effect on the future development of the subject." --Mathematical Reviews.