Article

Statistics and Computing

, Volume 16, Issue 4, pp 339-354

DRAM: Efficient adaptive MCMC

  • Heikki HaarioAffiliated withLappeenranta University of Technology Email author 
  • , Marko LaineAffiliated withLappeenranta University of Technology
  • , Antonietta MiraAffiliated withUniversity of Insubria
  • , Eero SaksmanAffiliated withUniversity of Jyväaskyläa

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

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.

Keywords

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