Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

Details Limit theorems for some adaptive MCMC algorithms with subgeometric kernels Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

2008-07-18

arxiv.org/abs/0807.2952v2

Mathematics

Probability

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This paper deals with the ergodicity and the existence of a strong law of large numbers for adaptive Markov Chain Monte Carlo. We show that a diminishing adaptation assumption together with a drift condition for positive recurrence is enough to imply ergodicity. Strengthening the drift condition to a polynomial drift condition yields a strong law of large numbers for possibly unbounded functions. These results broaden considerably the class of adaptive MCMC algorithms for which rigorous analysis is now possible. As an example, we give a detailed analysis of the Adaptive Metropolis Algorithm of Haario et al. (2001) when the target distribution is sub-exponential in the tails.

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