## Analysis of a Nonreversible Markov Chain Sampler

**
Persi Diaconis, Dept. of Statistics and Dept. of Mathematics,
Stanford University**

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).

**Associated reference:**
This is a revised version of the following technical report:
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.