arXiv 1102.21712011-02math.PRstat.CO

CLTs and asymptotic variance of time-sampled Markov chains

Krzysztof Latuszynski, Gareth O. Roberts

备注：: A small simulation illustrating theoretical results added

英文摘要：

For a Markov transition kernel $P$ and a probability distribution $ μ$ on nonnegative integers, a time-sampled Markov chain evolves according to the transition kernel $P_μ = \sum_k μ(k)P^k.$ In this note we obtain CLT conditions for time-sampled Markov chains and derive a spectral formula for the asymptotic variance. Using these results we compare efficiency of Barker's and Metropolis algorithms in terms of asymptotic variance.

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