Applied Statistical Decision TheoryDivision of Research, Graduate School of Business Adminitration, Harvard University, 1961 - Business & Economics - 356 pages "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 |
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Page 50
... Example 1. Consider again the Independent Normal process with known precision h and unknown mean = μ which was considered in the first example in Section 3.2.1 and again in the example in Section 3.2.2 . Since the integral - √。 k ( y ...
... Example 1. Consider again the Independent Normal process with known precision h and unknown mean = μ which was considered in the first example in Section 3.2.1 and again in the example in Section 3.2.2 . Since the integral - √。 k ( y ...
Page 51
... example , and we shall then go on to exploit the idea by applying it to the Independ- ent Normal process with both mean and precision unknown . As an artificial example , suppose that instead of considering the Bernoulli process in its ...
... example , and we shall then go on to exploit the idea by applying it to the Independ- ent Normal process with both mean and precision unknown . As an artificial example , suppose that instead of considering the Bernoulli process in its ...
Page 178
... example in which exact results can be obtained ; after this example has fixed the ideas , we shall develop a more pragmatic approach to the general problem . 6.2.2 . Example Returning to the problem of inventory control discussed in ...
... example in which exact results can be obtained ; after this example has fixed the ideas , we shall develop a more pragmatic approach to the general problem . 6.2.2 . Example Returning to the problem of inventory control discussed in ...
Contents
The Problem and the Two Basic Modes of Analysis | 3 |
Univariate Normalized Mass and Density Functions | 7 |
Combination of Formal and Informal Analysis | 17 |
Copyright | |
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Common terms and phrases
a₁ a₂ approximation assign assumption Bernoulli process beta function binomial choose compute conditional measure conjugate Conjugate prior cost cumulative function data-generating process decision maker decision problem decision tree defined definition denote estimate evaluated EVPI EVSI example expected terminal opportunity expected utility expected value experiment experimental outcome extensive form Ezle Figure follows gamma gamma-2 given h is known h is unknown k₁ k₂ kernel li(a li(e li(eo likelihood linear marginal measure mass function n₁ normalized density function observations obtain optimal act optimal sample parameter perfect information Poisson possible posterior density posterior distribution preposterior analysis prior density prior distribution prior expected probability quantity random variable sample information Section stopping process Substituting sufficient statistic Table terminal act terminal analysis terminal opportunity loss terminal utility theorem tion u₁ utility characteristic value of perfect vector vi(e