- Introduction to multicanonical Monte Carlo simulations by B. A. Berg MCMC in $I \times J \times K$ contingency tables by F. Bunea and J. Besag Extension of Fill's perfect rejection sampling algorithm to general chains (Extended abstract) by J. A. Fill, M. Machida, D. J. Murdoch, and J. S. Rosenthal Taming zero modes in lattice QCD with the polynomial hybrid Monte Carlo algorithm by K. Jansen Monte Carlo algorithms and non-local actions by A. D. Kennedy Towards a more general Propp-Wilson algorithm: Multistage backward coupling by X.-L. Meng On non-reversible Markov chains by A. Mira and C. J. Geyer Exact sampling for Bayesian inference: Unbounded state spaces by D. J. Murdoch Recent progress on computable bounds and the simple slice sampler by G. O. Roberts and J. S. Rosenthal MCMC methods in statistical mechanics: Avoiding quasi-ergodic problems by S. G. Whittington Layered multishift coupling for use in perfect sampling algorithms (with a primer on CFTP) by D. B. Wilson Introduction to semi Markov chain Monte Carlo by H. Ljung Accelerated simulation of ATM switching fabrics by A. R. Dabrowski, G. Lamothe, and D. R. McDonald Some stratagems for the estimation of time series using the Metropolis method by A. R. Runnalls Monte Carlo study of adsorption of interacting self-avoiding walks by T. Vrbova.
- (source: Nielsen Book Data)

This volume contains the proceedings of the Workshop on Monte Carlo Methods held at The Fields Institute for Research in Mathematical Sciences (Toronto, 1998). The workshop brought together researchers in physics, statistics, and probability. The papers in this volume - of the invited speakers and contributors to the poster session - represent the interdisciplinary emphasis of the conference. Monte Carlo methods have been used intensively in many branches of scientific inquiry.Markov chain methods have been at the forefront of much of this work, serving as the basis of many numerical studies in statistical physics and related areas since the Metropolis algorithm was introduced in 1953. Statisticians and theoretical computer scientists have used these methods in recent years, working on different fundamental research questions, yet using similar Monte Carlo methodology. This volume focuses on Monte Carlo methods that appear to have wide applicability and emphasizes new methods, practical applications and theoretical analysis. It will be of interest to researchers and graduate students who study and/or use Monte Carlo methods in areas of probability, statistics, theoretical physics, or computer science.

(source: Nielsen Book Data)