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Journal of Biopharmaceutical Statistics Volume 21, 2011 - Issue 6: Special Issue on Computer-Intensive Methods with Emphasis on Bootstrap and Medical Research Applications
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Original Articles

The Bootstrap and Markov-Chain Monte Carlo

Bradley Efron Stanford University, Stanford, California, USACorrespondence[email protected]
Pages 1052-1062 | Received 05 Feb 2011, Accepted 12 May 2011, Published online: 24 Oct 2011
 
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Abstract

This note concerns the use of parametric bootstrap sampling to carry out Bayesian inference calculations. This is only possible in a subset of those problems amenable to Markov-Chain Monte Carlo (MCMC) analysis, but when feasible the bootstrap approach offers both computational and theoretical advantages. The discussion here is in terms of a simple example, with no attempt at a general analysis.

ACKNOWLEDGMENT

Research supported in part by NIH grant 8R01 EB002784 and by NSF grant DMS 0804324.

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