eBook ISBN:  9781470430504 
Product Code:  FIC/26.E 
List Price:  $89.00 
MAA Member Price:  $80.10 
AMS Member Price:  $71.20 
eBook ISBN:  9781470430504 
Product Code:  FIC/26.E 
List Price:  $89.00 
MAA Member Price:  $80.10 
AMS Member Price:  $71.20 

Book DetailsFields Institute CommunicationsVolume: 26; 2000; 228 ppMSC: Primary 65; 60; Secondary 81; 82
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 copublished with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).
ReadershipGraduate students and researchers working in Monte Carlo methods in probability, statistics, theoretical physics, or computer science.

Table of Contents

Chapters

Bernd Berg — Introduction to multicanonical Monte Carlo simulations

Florentina Bunea and Julian Besag — MCMC in $I \times J \times K$ contingency tables

James Fill, Motoya Machida, Duncan Murdoch and Jeffrey Rosenthal — Extension of Fill’s perfect rejection sampling algorithm to general chains (Extended abstract)

Karl Jansen — Taming zero modes in lattice QCD with the polynomial hybrid Monte Carlo algorithm

A. Kennedy — Monte Carlo algorithms and nonlocal actions

XiaoLi Meng — Towards a more general ProppWilson algorithm: Multistage backward coupling

Antonietta Mira and Charles Geyer — On nonreversible Markov chains

D. Murdoch — Exact sampling for Bayesian inference: Unbounded state spaces

Gareth Roberts and Jeffrey Rosenthal — Recent progress on computable bounds and the simple slice sampler

Stuart Whittington — MCMC methods in statistical mechanics: Avoiding quasiergodic problems

David Wilson — Layered multishift coupling for use in perfect sampling algorithms (with a primer on CFTP)

Håkan Ljung — Introduction to semi Markov chain Monte Carlo

A. Dabrowski, G. Lamothe and D. McDonald — Accelerated simulation of ATM switching fabrics

Andrew Runnalls — Some stratagems for the estimation of time series using the Metropolis method

Tereza Vrbová — Monte Carlo study of adsorption of interacting selfavoiding walks


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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 copublished 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.

Chapters

Bernd Berg — Introduction to multicanonical Monte Carlo simulations

Florentina Bunea and Julian Besag — MCMC in $I \times J \times K$ contingency tables

James Fill, Motoya Machida, Duncan Murdoch and Jeffrey Rosenthal — Extension of Fill’s perfect rejection sampling algorithm to general chains (Extended abstract)

Karl Jansen — Taming zero modes in lattice QCD with the polynomial hybrid Monte Carlo algorithm

A. Kennedy — Monte Carlo algorithms and nonlocal actions

XiaoLi Meng — Towards a more general ProppWilson algorithm: Multistage backward coupling

Antonietta Mira and Charles Geyer — On nonreversible Markov chains

D. Murdoch — Exact sampling for Bayesian inference: Unbounded state spaces

Gareth Roberts and Jeffrey Rosenthal — Recent progress on computable bounds and the simple slice sampler

Stuart Whittington — MCMC methods in statistical mechanics: Avoiding quasiergodic problems

David Wilson — Layered multishift coupling for use in perfect sampling algorithms (with a primer on CFTP)

Håkan Ljung — Introduction to semi Markov chain Monte Carlo

A. Dabrowski, G. Lamothe and D. McDonald — Accelerated simulation of ATM switching fabrics

Andrew Runnalls — Some stratagems for the estimation of time series using the Metropolis method

Tereza Vrbová — Monte Carlo study of adsorption of interacting selfavoiding walks