Slow hit-and-run sampling

Claude Bélisle

Statistics & Probability Letters, 2000, vol. 47, issue 1, 33-43

Abstract: We show that the hit-and-run sampler can converge to its target distribution at an arbitrarily slow rate. We also illustrate how the speed of convergence of the hit-and-run sampler can be affected by small perturbations of the target distribution.

Keywords: Markov; chain; Monte; Carlo; methods; Hit-and-run; sampler; Gibbs; sampler; Geometric; ergodicity (search for similar items in EconPapers)
Date: 2000
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