Bounds on regeneration times and convergence rates for Markov chains

STOCHASTIC PROCESSES AND THEIR APPLICATIONS

卷:80

Article

Roberts, GO ; Tweedie, RL

关键词: renewal times;    geometric ergodicity;    rates of convergence;    Markov chain Monte Carlo;    shift coupling;    computable bounds;   

DOI  :  10.1016/S0304-4149(98)00085-4

来源: Elsevier

【 摘 要 】

In many applications of Markov chains, and especially in Markov chain Monte Carlo algorithms, the rate of convergence of the chain is of critical importance. Most techniques to establish such rates require bounds on the distribution of the random regeneration time T that can be constructed, via splitting techniques, at times of return to a small set C satisfying a minorisation condition P(x, .) greater than or equal to epsilon phi(.), x is an element of C. Typically, however, it is much easier to get bounds on the time tau(C) of return to the small set itself, usually based on a geometric drift function PV less than or equal to lambda V + b1(C), where PV(x) = E-x(V(X-1)). We develop a new relationship between T and tau(C), and this gives a bound on the tail of T, based on epsilon, lambda and b, which is a strict improvement on existing results. When evaluating rates of convergence we see that our bound usually gives considerable numerical improvement on previous expressions. (C) 1999 Elsevier Science B.V. All rights reserved.

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