Explicit bounds for geometric convergence of Markov chains
This paper presents bounds on convergence rates of Markov chains in terms of quantities calculable directly from chain transition operators. Bounds are constructed by creating a probability distribution that minorizes the transition kernel over some region, and by examining bounds on an expectation conditional on lying within and without this region. These are shown to be sharper in most cases than previous similar results. These bounds are applied to a Markov chain useful in frequentist conditional inference in canonical generalized linear models.
Permanent link to this document: http://projecteuclid.org/euclid.jap/1014842825
Digital Object Identifier: doi:10.1239/jap/1014842825
Mathematical Reviews number (MathSciNet): MR1782442
Zentralblatt MATH identifier: 0970.60084