The Multiple-Try Method and Local Optimization in Metropolis Sampling

@article{Liu2000TheMM,
  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},
  year={2000},
  volume={95},
  pages={121 - 134}
}
  • J. Liu, F. Liang, W. Wong
  • Published 2000
  • Mathematics
  • Journal of the American Statistical Association
  • 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… CONTINUE READING
    A tutorial on adaptive MCMC
    • 641
    • PDF
    Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC
    • 112
    • PDF
    Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling
    • 58
    • PDF
    Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation
    • 276
    • PDF
    Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
    • 191
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 27 REFERENCES