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 36
... Stopping 2.3.1 . Data - Generating Processes and Stopping Processes In many situations the complete description of ... process , and the description of this process constitutes the first part of the description of the experiment ...
... Stopping 2.3.1 . Data - Generating Processes and Stopping Processes In many situations the complete description of ... process , and the description of this process constitutes the first part of the description of the experiment ...
Page 37
... stopping process such that , given a particular value of the parameter 01 which characterizes the data - generating process and a particular value of another ( scalar or vector ) state parameter 02 , the probability that a first ...
... stopping process such that , given a particular value of the parameter 01 which characterizes the data - generating process and a particular value of another ( scalar or vector ) state parameter 02 , the probability that a first ...
Page 39
... process until r 1's have occurred ( or has $ r to spend and each observation costs $ 1 which is refunded if = 0 ) . The stopping probabilities are ( 1 if ( k + 1x1 , ... Xk ; 01 , 02 ) Σk = 1x¡ < r , = if Σ = 1X ; = r and = s ( nz ; 01 ...
... process until r 1's have occurred ( or has $ r to spend and each observation costs $ 1 which is refunded if = 0 ) . The stopping probabilities are ( 1 if ( k + 1x1 , ... Xk ; 01 , 02 ) Σk = 1x¡ < r , = if Σ = 1X ; = r and = s ( nz ; 01 ...
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