Ergodicity of Markov chain Monte Carlo with reversible proposal
Methodology
2016-02-10 v1 Statistics Theory
Statistics Theory
Abstract
We describe ergodic properties of some Metropolis-Hastings (MH) algorithms for heavy-tailed target distributions. The analysis usually falls into sub-geometric ergodicity framework but we prove that the mixed preconditioned Crank-Nicolson (MpCN) algorithm has geometric ergodicity even for heavy-tailed target distributions. This useful property comes from the fact that the MpCN algorithm becomes a random-walk Metropolis algorithm under suitable transformation.
Keywords
Cite
@article{arxiv.1602.02889,
title = {Ergodicity of Markov chain Monte Carlo with reversible proposal},
author = {Kengo Kamatani},
journal= {arXiv preprint arXiv:1602.02889},
year = {2016}
}
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14 pages