Quantitative Non-Geometric Convergence Bounds for Independence Samplers

Gareth O. Roberts () and Jeffrey S. Rosenthal ()
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Gareth O. Roberts: Lancaster University
Jeffrey S. Rosenthal: University of Toronto

Methodology and Computing in Applied Probability, 2011, vol. 13, issue 2, 391-403

Abstract: Abstract We provide precise, rigorous, fairly sharp quantitative upper and lower bounds on the time to convergence of independence sampler MCMC algorithms which are not geometrically ergodic. This complements previous work on the geometrically ergodic case. Our results illustrate that even simple-seeming Markov chains often converge extremely slowly, and furthermore slight changes to a parameter value can have an enormous effect on convergence times.

Keywords: Markov chain; MCMC; Independence sampler; Convergence bounds; Primary 60J05; Secondary 60J22, 68W20 (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s11009-009-9157-z

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