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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
Monte Carlo Methods
eBook ISBN:  978-1-4704-3050-4
Product Code:  FIC/26.E
List Price: $89.00
MAA Member Price: $80.10
AMS Member Price: $71.20
Monte Carlo Methods
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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
eBook ISBN:  978-1-4704-3050-4
Product Code:  FIC/26.E
List Price: $89.00
MAA Member Price: $80.10
AMS Member Price: $71.20
  • Book Details
     
     
    Fields Institute Communications
    Volume: 262000; 228 pp
    MSC: 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 co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).

    Readership

    Graduate 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 non-local actions
    • Xiao-Li Meng — Towards a more general Propp-Wilson algorithm: Multistage backward coupling
    • Antonietta Mira and Charles Geyer — On non-reversible 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 quasi-ergodic 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 self-avoiding walks
  • Requests
     
     
    Review Copy – for publishers of book reviews
    Accessibility – to request an alternate format of an AMS title
Volume: 262000; 228 pp
MSC: 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 co-published with the Fields Institute for Research in Mathematical Sciences (Toronto, Ontario, Canada).

Readership

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 non-local actions
  • Xiao-Li Meng — Towards a more general Propp-Wilson algorithm: Multistage backward coupling
  • Antonietta Mira and Charles Geyer — On non-reversible 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 quasi-ergodic 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 self-avoiding walks
Review Copy – for publishers of book reviews
Accessibility – to request an alternate format of an AMS title
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