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- W1969131157 abstract "Expected gain in Shannon information is commonly suggested as a Bayesian design evaluation criterion. Because estimating expected information gains is computationally expensive, examples in which they have been successfully used in identifying Bayes optimal designs are both few and typically quite simplistic. This article discusses in general some properties of estimators of expected information gains based on Markov chain Monte Carlo (MCMC) and Laplacian approximations. We then investigate some issues that arise when applying these methods to the problem of experimental design in the (technically nontrivial) random fatigue-limit model of Pascual and Meeker. An example comparing follow-up designs for a laminate panel study is provided." @default.
- W1969131157 created "2016-06-24" @default.
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- W1969131157 date "2003-09-01" @default.
- W1969131157 modified "2023-10-17" @default.
- W1969131157 title "Estimating Expected Information Gains for Experimental Designs With Application to the Random Fatigue-Limit Model" @default.
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- W1969131157 doi "https://doi.org/10.1198/1061860032012" @default.
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