Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II
Probability
2009-11-03 v1 Statistics Theory
Computation
Statistics Theory
Abstract
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.
Cite
@article{arxiv.0911.0221,
title = {Limit theorems for some adaptive MCMC algorithms with subgeometric kernels: Part II},
author = {Yves F. Atchade and Gersende Fort},
journal= {arXiv preprint arXiv:0911.0221},
year = {2009}
}
Comments
34 pages