Susan Holmes, Dept. of Statistics, Stanford University and Unité de Biométrie, INRA-Montpellier

Radford M. Neal, Dept. of Statistics and Dept. of Computer Science, University of Toronto

We analyse the convergence to stationarity of a simple non-reversible Markov chain that serves as a model for several non-reversible Markov chain sampling methods that are used in practice. Our theoretical and numerical results show that non-reversibility can indeed lead to improvements over the diffusive behavior of simple Markov chain sampling schemes. The analysis uses both probabilistic techniques and an explicit diagonalisation.

*Annals of Applied Probability*, vol. 10, pp. 726-752 (2000).

Diaconis, P., Holmes, S., and Neal, R. M. (1997) ``Analysis of a non-reversible Markov chain sampler'', Technical Report BU-1385-M, Biometrics Unit, Cornell University, 26 pages: abstract, postscript, pdf.