DRAM: Efficient adaptive MCMC |
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Authors: | Heikki Haario Marko Laine Antonietta Mira Eero Saksman |
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Institution: | 1. Lappeenranta University of Technology, Lappeenranta, Finland 2. University of Insubria, Varese, Italy 3. University of Jyv?askyl?a, Jyv?askyl?a, Finland
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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. |
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Keywords: | Adaptive Markov chain Monte Carlo Adaptive Metropolis-Hastings Delayed rejection Efficiency ordering |
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