The Multiple-Try Method and Local Optimization in Metropolis Sampling

  title={The Multiple-Try Method and Local Optimization in Metropolis Sampling},
  author={J. Liu and F. Liang and W. Wong},
  journal={Journal of the American Statistical Association},
  pages={121 - 134}
Abstract This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together with adaptive direction sampling, we propose a novel method for incorporating local optimization steps into a MCMC sampler in continuous state-space. Numerical studies show that the new method performs significantly better than the traditional Metropolis-Hastings (M-H) sampler. With minor tailoring in using… Expand
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