## 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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### Contents

The Problem and the Two Basic Modes of Analysis | 3 |

Combination of Formal and Informal Analysis | 17 |

Prior Weights and Consistent Behavior | 25 |

Copyright | |

22 other sections not shown

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### Common terms and phrases

approximation assign Bernoulli process beta function binomial Chapter compute conditional measure Conjugate prior cost cumulative function data-generating process decision maker decision problem defined definition denned denote distribution of ft distribution with parameter estimate evaluated EVPI EVSI example expected terminal opportunity expected utility expected value experiment experimental outcome Figure formula gamma gamma-2 given h is known h is unknown implies Independent Normal process joint density joint distribution kernel likelihood linear linear-loss integrals marginal density marginal distribution marginal likelihood mass function matrix mean and variance Normal distribution Normal-gamma normalized density function observations obtain optimal act optimal sample perfect information Poisson Poisson process positive-definite possible posterior distribution precision h preposterior analysis prior density prior distribution proof prove quantity random variable sample information Section stopping process Substituting sufficient statistic Table terminal act terminal analysis terminal opportunity loss terminal utility theorem tion unconditional distribution univariate ut(a vector