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In discrete time, studies of quantitative convergence rates have been motivated largely by Markov chain Monte Carlo algorithms (see Gelfand and Smith, 1990; Smith and Roberts,
Keywords: General state space Markov chains; f -regularity; Markov chain central limit theo- rem; Drazin inverse; fundamental matrix; asymptotic
A question of increasing importance in the Markov chain Monte Carlo literature (Gelfand and Smith, 1990; Smith and Roberts, 1993) is the issue of geometric ergodicity of Markov
Central limit theorems for functionals of general state space Markov chains are of crucial importance in sensible implementation of Markov chain Monte Carlo algorithms as well as
The bound follows from an estimation of the relaxation speed of chains with complete connections with summable decay, which is obtained via a explicit coupling between pairs of
Abstract: Let X and Y be time-homogeneous Markov processes with common state space E, and assume that the transition kernels of X and Y admit densities with respect to
Molloy, Very rapidly mixing Markov chains for 2∆-colourings and for independent sets in a 4-regular graph, Random Struc. Tetali, Mathematical aspects of mixing times in Markov
In fact it is standard to obtain estimates on the exit time distribution of a reversible Markov process from a region if one has an estimate from below of the spectral gap of
