The bootstrap and Markov-chain Monte Carlo.
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.
Stanford University, Stanford, California 94305, USA. brad@stat.stanford.edu
Bradley
B
Efron
D001499:Bayes Theorem; D008390:Markov Chains; D009010:Monte Carlo Method