The Annals of Applied Probability

Markov chain decomposition for convergence rate analysis

Neal Madras and Dana Randall

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In this paper we develop tools for analyzing the rate at which a reversible Markov chain converges to stationarity. Our techniques are useful when the Markov chain can be decomposed into pieces which are themselves easier to analyze. The main theorems relate the spectral gap of the original Markov chains to the spectral gaps of the pieces. In the first case the pieces are restrictions of the Markov chain to subsets of the state space; the second case treats a Metropolis--Hastings chain whose equilibrium distribution is a weighted average of equilibrium distributions of other Metropolis--Hastings chains on the same state space.

Article information

Ann. Appl. Probab., Volume 12, Number 2 (2002), 581-606.

First available in Project Euclid: 17 July 2002

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Zentralblatt MATH identifier

Primary: 60J05: Discrete-time Markov processes on general state spaces
Secondary: 65C05: Monte Carlo methods 68Q25: Analysis of algorithms and problem complexity [See also 68W40]

Markov chain Monte Carlo spectral gap Metropolis-Hastings algorithm simulated tempering decomposition


Madras, Neal; Randall, Dana. Markov chain decomposition for convergence rate analysis. Ann. Appl. Probab. 12 (2002), no. 2, 581--606. doi:10.1214/aoap/1026915617.

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