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© 2018

Path Coupling and Aggregate Path Coupling

Book

Table of contents

  1. Front Matter
    Pages i-xi
  2. Yevgeniy Kovchegov, Peter T. Otto
    Pages 1-22
  3. Yevgeniy Kovchegov, Peter T. Otto
    Pages 23-36
  4. Yevgeniy Kovchegov, Peter T. Otto
    Pages 37-51
  5. Yevgeniy Kovchegov, Peter T. Otto
    Pages 53-54
  6. Yevgeniy Kovchegov, Peter T. Otto
    Pages 55-64
  7. Yevgeniy Kovchegov, Peter T. Otto
    Pages 65-79
  8. Yevgeniy Kovchegov, Peter T. Otto
    Pages 81-90
  9. Back Matter
    Pages 91-96

About this book

Introduction

This book describes and characterizes an extension to the classical path coupling method applied to statistical mechanical models, referred to as aggregate path coupling.  In conjunction with large deviations estimates, the aggregate path coupling method is used to prove rapid mixing of Glauber dynamics for a large class of statistical mechanical models, including models that exhibit discontinuous phase transitions which have traditionally been more difficult to analyze rigorously.  The book shows how the parameter regions for rapid mixing for several classes of statistical mechanical models are derived using the aggregate path coupling method.

Keywords

Path Coupling Aggregate Path Coupling Statistical Mechanical Models Gibbs Measure Glauber Dynamics Large Deviation Principle Mixing Time Equlibrium Phase Transition

Authors and affiliations

  1. 1.Department of MathematicsOregon State UniversityCorvallisUSA
  2. 2.Department of MathematicsWillamette UniversitySalemUSA

Bibliographic information

  • Book Title Path Coupling and Aggregate Path Coupling
  • Authors Yevgeniy Kovchegov
    Peter T. Otto
  • Series Title SpringerBriefs in Probability and Mathematical Statistics
  • Series Abbreviated Title SpringerBriefs in Probabil., Math.Statist.
  • DOI https://doi.org/10.1007/978-3-319-77019-2
  • Copyright Information The Author(s), under exclusive licence to Springer International Publishing AG, part of Springer Nature 2018 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
  • Softcover ISBN 978-3-319-77018-5
  • eBook ISBN 978-3-319-77019-2
  • Series ISSN 2365-4333
  • Series E-ISSN 2365-4341
  • Edition Number 1
  • Number of Pages XI, 96
  • Number of Illustrations 8 b/w illustrations, 4 illustrations in colour
  • Topics Probability Theory and Stochastic Processes
  • Buy this book on publisher's site
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