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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.