Convergence of Griddy Gibbs sampling and other perturbed Markov chains

ABSTRACTThe Griddy Gibbs sampling was proposed by Ritter and Tanner [Facilitating the Gibbs Sampler: the Gibbs Stopper and the Griddy–Gibbs Sampler. J Am Stat Assoc. 1992;87(419):861—868] as a computationally efficient approximation of the well-known Gibbs sampling method. The algorithm is simple and effective and has been used successfully to address problems in various fields of applied science. However, the approximate nature of the algorithm has prevented it from being widely used: the Markov chains generated by the Griddy Gibbs sampling method are not reversible in general, so the existence and uniqueness of its invariant measure is not guaranteed. Even when such an invariant measure uniquely exists, there was no estimate of the distance between it and the probability distribution of interest, hence no means to ensure the validity of the algorithm as a means to sample from the true distribution. In this paper, we show, subject to some fairly natural conditions, that the Griddy Gibbs method has a uniq...