Applied Statistical Decision Theory (Wiley Classics Library)

Applied Statistical Decision Theory (Wiley Classics Library)

Yazar : Howard Raiffa
Yayınevi : Wiley-Interscience
ISBN : 9780471383499
Baskı yılı : 2000
Sayfa sayısı : 388
Ağırlık : 0.51 kg
Stok durumu : Tükendi   
This volume presents Raiffa and Schlaifers groundbreaking examination of the applications of Bayesian statistical theory to real-life business problems of decision under conditions of uncertainty. The emphasis is on identifying a course of action that is logically consistent with the decision makers own preference for consequences, as expressed by numerical utilities, and with the weight s/he attaches to unknown variables, as expressed by numerical probabilities. Well-organized 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 major work and a major stimulus to further work. 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.
Foreword
Preface and Introduction
1 The Problem and the Two Basic Modes of Analysis 3
2 Sufficient Statistics and Noninformative Stopping 28
3 Conjugate Prior Distributions 43
4 Additive Utility, Opportunity Loss, and the Value of Information: Introduction to Part II 79
5A Linear Terminal Analysis 93
5B Selection of the Best of Several Processes 139
6 Problems in Which the Act and State Spaces Coincide 176
7 Univariate Normalized Mass and Density Functions 211
A Natural Univariate Mass and Density Functions 213
B Compound Univariate Mass and Density Functions 232
8 Multivariate Normalized Density Functions 242
9 Bernoulli Process 261
10 Poisson Process 275
11 Independent Normal Process 290
A Mean Known 290
B Precision Known 294
C Neither Parameter Known 298
12 Independent Multinormal Process 310
A Precision Known 310
B Relative Precision Known 316
C Interrelated Univariate Normal Processes 326
13 Normal Regression Process 334
A Precision Known 336
B Precision Unknown 342
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