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• W2161395159 abstract "This paper considers ergodicity properties of certain adaptive Markov chain Monte Carlo(MCMC) algorithms for multidimensional target distributions, in particular Adaptive Metropolisand Adaptive Metropolis-within-Gibbs. It was previously shown (Roberts and Rosenthal [21])that Diminishing Adaptation and Containment imply ergodicity of adaptive MCMC. We derivevarious sufficient conditions to ensure Containment, and connect the convergence rates ofalgorithms with the tail properties of the corresponding target distributions. An example isgiven to show that Diminishing Adaptation alone does not imply ergodicity. We also present aSummable Adaptive Condition which, when satisfied, proves ergodicity more easily." @default.
• W2161395159 created "2016-06-24" @default.
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• W2161395159 date "2009-01-01" @default.
• W2161395159 modified "2023-10-17" @default.
• W2161395159 title "On the containment condition for adaptive Markov chain Monte Carlo algorithms" @default.
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