Statistics and Computing

, Volume 16, Issue 4, pp 339–354 | Cite as

DRAM: Efficient adaptive MCMC

  • Heikki HaarioEmail author
  • Marko Laine
  • Antonietta Mira
  • Eero Saksman


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 distributions are not available. Similarly, delayed rejection provides a systematic remedy when the adaptation process has a slow start.


Adaptive Markov chain Monte Carlo Adaptive Metropolis-Hastings Delayed rejection Efficiency ordering 


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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Heikki Haario
    • 1
    Email author
  • Marko Laine
    • 1
  • Antonietta Mira
    • 2
  • Eero Saksman
    • 3
  1. 1.Lappeenranta University of TechnologyLappeenrantaFinland
  2. 2.University of InsubriaVareseItaly
  3. 3.University of JyväaskyläaJyväaskyläaFinland

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