Adaptive Independent Metropolis–Hastings by Fast Estimation of Mixtures of Normals

@article{Giordani2008AdaptiveIM,
  title={Adaptive Independent Metropolis–Hastings by Fast Estimation of Mixtures of Normals},
  author={P. Giordani and R. Kohn},
  journal={Journal of Computational and Graphical Statistics},
  year={2008},
  volume={19},
  pages={243 - 259}
}
  • P. Giordani, R. Kohn
  • Published 2008
  • Computer Science, Mathematics
  • Journal of Computational and Graphical Statistics
Adaptive Metropolis–Hastings samplers use information obtained from previous draws to tune the proposal distribution automatically and repeatedly. Adaptation needs to be done carefully to ensure convergence to the correct target distribution because the resulting chain is not Markovian. We construct an adaptive independent Metropolis–Hastings sampler that uses a mixture of normals as a proposal distribution. To take full advantage of the potential of adaptive sampling our algorithm updates the… Expand
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