Applied Statistical Decision Theory"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 |
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 binomial compute conditional measure conjugate Conjugate prior cost cumulative function data-generating process decision maker decision problem defined definition denote e₁ estimate evaluated EVPI EVSI example expected terminal opportunity expected utility expected value experiment experimental outcome Ezle Figure follows formula gamma gamma-1 given h is known h is unknown Independent Normal process k₁ k₂ kernel li(a likelihood linear linear-loss integrals marginal measure mass function matrix mean and variance normalized density function observations obtain optimal act optimal sample perfect information Poisson Poisson process possible posterior density posterior distribution preposterior analysis prior density prior distribution prove quantity random variable Section stopping process substituting sufficient statistic Table terminal act terminal analysis terminal opportunity loss terminal utility theorem tion u₁ vector vi(e