CLTs and asymptotic variance of time-sampled Markov chains

2011/02/10 by Latuszynski, Krzysztof, Roberts, Gareth O. · 2 citations
#Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Probability (math.PR)

paper · doi:10.48550/arxiv.1102.2171

Abstract

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 = ∑_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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