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Ergodicity of Markov chain Monte Carlo with reversible proposal

  • K. Kamatani (a1)


We describe the ergodic properties of some Metropolis–Hastings algorithms for heavy-tailed target distributions. The results of these algorithms are usually analyzed under a subgeometric ergodic 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, under a suitable transformation, the MpCN algorithm becomes a random-walk Metropolis algorithm.


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* Postal address: Graduate School of Engineering Science and Center for Mathematical Modeling and Data Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan. Email address:


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Ergodicity of Markov chain Monte Carlo with reversible proposal

  • K. Kamatani (a1)


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