CLTs and asymptotic variance of time-sampled Markov chains
Latuszynski, Krzysztof · Roberts, Gareth O.
Original · EN
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 Pμ = ∑ₖ μ(k)Pᵏ. 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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