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
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The Problem and the Two Basic Modes of Analysis
Combination of Formal and Informal Analysis
Prior Weights and Consistent Behavior
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approximation assign Bernoulli process beta function binomial compute Conjugate prior cost cumulative function cumulative probabilities decision maker decision problem defined definition denote distribution of h distribution with parameter estimate evaluated EVPI EVSI example expected terminal opportunity expected utility expected value experiment Figure gamma gamma-2 given h is known h is unknown implies Independent Normal process inverted-beta-1 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 posterior distribution precision h preposterior analysis prior density prior distribution prior expected proof prove quantity random variable sample information Section Substituting sufficient statistic Table terminal act terminal analysis terminal opportunity loss Theorem tion unconditional distribution univariate ut(a value of perfect vector write