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How Many Samples?: A Bayesian Nonparametric Approach
Stephen G. Walker
Journal of the Royal Statistical Society. Series D (The Statistician)
Vol. 52, No. 4 (2003), pp. 475-482
Stable URL: http://www.jstor.org/stable/4128126
Page Count: 8
You can always find the topics here!Topics: Expected utility, Approximation, Utility functions, Distribution functions, State of nature, Mathematical problems, Density, Simulations, Nonparametric models, Statism
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The paper considers a Bayesian nonparametric decision theoretic approach to sample size calculations, where the ultimate goal is to make a terminal action from a finite set of actions. This terminal action is made via the maximization of expected utility, the maximization being made with respect to a probability measure on the states of nature. The probability measure depends on the amount of information, i.e. the number of samples collected. It is the prior in the case of no samples and the posterior when samples have been taken.
Journal of the Royal Statistical Society. Series D (The Statistician) © 2003 Royal Statistical Society