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Monte Carlo Methods
Edited by: Neal Madras, York University, Toronto, ON, Canada
A co-publication of the AMS and Fields Institute.

Fields Institute Communications
2000; 228 pp; hardcover
Volume: 26
ISBN-10: 0-8218-1992-5
ISBN-13: 978-0-8218-1992-0
List Price: US$84
Member Price: US$67.20
Order Code: FIC/26
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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.

Titles in this series are co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).


Graduate students and researchers working in Monte Carlo methods in probability, statistics, theoretical physics, or computer science.

Table of Contents

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