First Hitting Time Analysis of the Independence Metropolis Sampler

Romeo Maciuca () and Song-Chun Zhu ()
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Romeo Maciuca: University of California
Song-Chun Zhu: University of California

Journal of Theoretical Probability, 2006, vol. 19, issue 1, 235-261

Abstract: In this paper, we study a special case of the Metropolis algorithm, the Independence Metropolis Sampler (IMS), in the finite state space case. The IMS is often used in designing components of more complex Markov Chain Monte Carlo algorithms. We present new results related to the first hitting time of individual states for the IMS. These results are expressed mostly in terms of the eigenvalues of the transition kernel. We derive a simple form formula for the mean first hitting time and we show tight lower and upper bounds on the mean first hitting time with the upper bound being the product of two factors: a “local” factor corresponding to the target state and a “global” factor, common to all the states, which is expressed in terms of the total variation distance between the target and the proposal probabilities. We also briefly discuss properties of the distribution of the first hitting time for the IMS and analyze its variance. We conclude by showing how some non-independence Metropolis–Hastings algorithms can perform better than the IMS and deriving general lower and upper bounds for the mean first hitting times of a Metropolis–Hastings algorithm.

Keywords: Eigenanalysis; expectation; first hitting time; independence Metropolis Sampler; Metropolis–Hastings (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1007/s10959-006-0002-9

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