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 |
From inside the book
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Page 6
... Decision Problem as a Game The general decision problem is : Given E , Z , A , o , u , and Po , ze , how should the decision maker choose an e and then , having observed z , choose an a , in such a way as to maximize his expected ...
... Decision Problem as a Game The general decision problem is : Given E , Z , A , o , u , and Po , ze , how should the decision maker choose an e and then , having observed z , choose an a , in such a way as to maximize his expected ...
Page 19
... Decision Tree Besides cutting the decision tree before it is logically complete , the decision maker may rationally decide not to make a complete formal analysis of even the truncated tree which he has constructed . Thus if E consists ...
... Decision Tree Besides cutting the decision tree before it is logically complete , the decision maker may rationally decide not to make a complete formal analysis of even the truncated tree which he has constructed . Thus if E consists ...
Page 43
... decision maker to assign a prior prob- ability to each individually and then verify the consistency of these assignments and make adjustments where necessary . In most applied problems the number of possible states will be extremely ...
... decision maker to assign a prior prob- ability to each individually and then verify the consistency of these assignments and make adjustments where necessary . In most applied problems the number of possible states will be extremely ...
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 Bernoulli process beta function beta-binomial binomial choose compute conditional measure conjugate Conjugate prior cost cumulative function data-generating process decision maker decision problem definition denote estimate evaluated EVPI EVSI example expected terminal opportunity expected utility expected value experiment experimental outcome extensive form Figure follows gamma gamma function gamma-1 given h is known h is unknown k₁ k₂ kernel li(e li(eo likelihood linear linear-loss integrals marginal measure matrix mean normalized density function observed 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 Section stopping process Substituting sufficient statistic Table terminal act terminal analysis terminal opportunity loss terminal utility theorem tion u₁ utility characteristic value of perfect variance vector vi(e