Introduction The Problem of Decision under Uncertainty 1
1．The Meaning of Probability 2
2．Expected Value and Utility 24
3．Random Variables and Probability Distributions 50
Part One The Use of Probabilities Based Directly on Experience 65
4．The Simplest Problems of Inventory Control; Incremental Analysis 66
5．Measures of Location: Fractiles and Expectations; Linear Profits and Costs 79
6．Assessment of Probabilities by Smoothing Historical Frequencies 95
7．Opportunity Loss and the Cost of Uncertainty 117
8．Lump-sum Losses; Scrap Allowances 133
Part Two Simple Random Processes and Derived Probabilities 159
9．Conditional and Joint Probability 160
10．The Bernoulli Process: The Binomial Distribution 174
11．The Bernoulli Process: The Pascal Distribution 183
12．Conditional Models and Marginal Probability 194
13．The Poisson Process: The Poisson Distribution 209
14．The Poisson Process: The Gamma Distribution 221
15．Min-Max Inventory Control 236
16．Measures of Dispersion: The Variance and the Standard Deviation 260
17．The Normal Approximation to Distributions of Sums of Random Variables 274
18．The Normal Approximation to Empirical Distributions 294
19．Waiting Lines 306
20．The Monte Carlo Method 320
Part Three The Use of Information Obtained by Sampling 329
21．Revision of Probabilities in the Light of New Information 330
22．Two-action Problems with Linear Costs 342
23．Samples from Finite Populations: The Hypergeometric Distribution 355
24．Interdependent Decision Problems; Finite vs．Infinite Populations 371
25．Samples from Many-valued Populations; Sufficient Statistics 384
26．Samples from “Normal” Populations with Known Variance 397
27．Samples from “Normal” Populations with Known Mean 405
28．Nuisance Parameters:“Normal” Populations with Both ParametersUnknown 413
29．Populations of Incompletely Specified Form; “Large-sample Theory” 423
30．Normal Prior Distributions 435
31．Biased Measurement and Biased Selection 458
32．Comparison of Two Unknown Quantities; the Importance of Sample Design 486
Part Four The Value of Additional Information 507
33．Evaluation of a Decision to Sample and Then Act; Preposterior Analysis 508
34．Two-action Problems with Linear Costs: Expected Loss and the Prior Dis-tribution of the Posterior Mean 519
35．Two-action Problems with Linear Costs: Optimal Sample Size 536
36．Interdependent Two-action Problems under a Stationary Distribution 553
37．Many-action Problems with Proportional Losses; General-purpose Estima-tion 574
38．Sequential Decision Procedures 590
Part Five Objectivist Statistics: Tests of Significance and Confidence Intervals 605
39．The Classical Theory of Testing Hypotheses 606
40．Evaluation of Statistical Decision Rules in Terms of Expected Loss 624
41．Tests of Significance as Sequential Decision Procedures 644
42．Confidence Intervals 661
Appendix 669
Continuous Prior Distributions for the Parameters of Bernoulli and Poisson Processes 670
Tables 679
Ⅰ．Cumulative Binomial Distribution 680
Ⅱ．Unit Normal Probability Distribution 702
Ⅲ．Cumulative Unit Normal Distribution 704
Ⅳ．Unit Normal Loss Integral 706
Ⅴ．Random Digits 708
Ⅵ．Square Roots 709
Ⅶ．Cube Roots 710
Charts 711
Ⅰ．Cumulative Poisson and Gamma Distributions 711
Ⅱ．Optimal Sample Size: Two-action Problems with Linear Costs 712
Ⅲ．Gamma Probability Distribution 713
Ⅳ．Unit Normal Distribution: Ratio of Ordinate to Left Tail 715
Ⅴ．x/？f Probability Distribution 716
Ⅵ．Optimal Sample Size: Many-action Problems with Proportional Losses 718
Index of Symbols 719
Subject Index 723