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arXiv:0911.0221 [math.PR]AbstractReferencesReviewsResources

Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II

Yves F. Atchade, Gersende Fort

Published 2009-11-02Version 1

We prove a central limit theorem for a general class of adaptive Markov Chain Monte Carlo algorithms driven by sub-geometrically ergodic Markov kernels. We discuss in detail the special case of stochastic approximation. We use the result to analyze the asymptotic behavior of an adaptive version of the Metropolis Adjusted Langevin algorithm with a heavy tailed target density.

Comments: 34 pages
Categories: math.PR, math.ST, stat.CO, stat.TH
Subjects: 60J10, 65C05
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