DRAM: Efficient adaptive MCMC

  title={DRAM: Efficient adaptive MCMC},
  author={Heikki Haario and Marko Laine and Antonietta Mira and Eero Saksman},
  journal={Statistics and Computing},
We propose to combine two quite powerful ideas that have recently appeared in the Markov chain Monte Carlo literature: adaptive Metropolis samplers and delayed rejection. The ergodicity of the resulting non–Markovian sampler is proved, and the efficiency of the combination is demonstrated with various examples. We present situations where the combination outperforms the original methods: adaptation clearly enhances efficiency of the delayed rejection algorithm in cases where good proposal… CONTINUE READING
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  • H. Haario
  • Saksman and J. Tamminen: Componentwise adaptation…
  • 2005
Highly Influential
11 Excerpts


  • A. G. Gelman
  • O. Roberts and W. R. Gilks: Efficient Metropolis…
  • 1996
Highly Influential
3 Excerpts

Componentwise adaptation for high dimensional MCMC

  • E. Saksman, J. Tamminen
  • Computational statistics
  • 2005

Rosenthal : On Adaptive Markov Chain Monte Carlo Algorithms

  • Y. F. Atchade, S. J.
  • 2005

Moulines : On the ergodicity properties of some adaptive MCMC algorithms

  • E.
  • Preprint
  • 2002

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