
Markov Chains and Mixing Times
American Mathematical Society (Publisher)
2nd Edition
Published on 30. October 2017
Book
Hardback
447 pages
978-1-4704-2962-1 (ISBN)
Shipment within 10-20 days
Description
This book is an introduction to the modern theory of Markov chains, whose goal is to determine the rate of convergence to the stationary distribution, as a function of state space size and geometry. This topic has important connections to combinatorics, statistical physics, and theoretical computer science. Many of the techniques presented originate in these disciplines.
The central tools for estimating convergence times, including coupling, strong stationary times, and spectral methods, are developed. The authors discuss many examples, including card shuffling and the Ising model, from statistical mechanics, and present the connection of random walks to electrical networks and apply it to estimate hitting and cover times.
The first edition has been used in courses in mathematics and computer science departments of numerous universities. The second edition features three new chapters (on monotone chains, the exclusion process, and stationary times) and also includes smaller additions and corrections throughout. Updated notes at the end of each chapter inform the reader of recent research developments.
The central tools for estimating convergence times, including coupling, strong stationary times, and spectral methods, are developed. The authors discuss many examples, including card shuffling and the Ising model, from statistical mechanics, and present the connection of random walks to electrical networks and apply it to estimate hitting and cover times.
The first edition has been used in courses in mathematics and computer science departments of numerous universities. The second edition features three new chapters (on monotone chains, the exclusion process, and stationary times) and also includes smaller additions and corrections throughout. Updated notes at the end of each chapter inform the reader of recent research developments.
Reviews / Votes
"Mixing times are an active research topic within many fields from statistical physics to the theory of algorithms, as well as having intrinsic interest within mathematical probability and exploiting discrete analogs of important geometry concepts. The first edition became an instant classic, being accessible to advanced undergraduates and yet bringing readers close to current research frontiers. This second edition adds chapters on monotone chains, the exclusion process and hitting time parameters. Having both exercises and citations to important research papers it makes an outstanding basis for either a lecture course or self-study." - David Aldous, University of California, Berkeley"Mixing time is the key to Markov chain Monte Carlo, the queen of approximation techniques. With new chapters on monotone chains, exclusion processes, and set-hitting, Markov Chains and Mixing Times is more comprehensive and thus more indispensable than ever. Prepare for an eye-opening mathematical tour!" - Peter Winkler, Dartmouth College
"The study of finite Markov chains has recently attracted increasing interest from a variety of researchers. This is the second edition of a very valuable book on the subject. The main focus is on the mixing time of Markov chains, but there is a lot of additional material. In this edition, the authors have taken the opportunity to add new material and bring the reader up to date on the latest research. I have used the first edition in a graduate course and I look forward to using this edition for the same purpose in the near future." - Alan Frieze, Carnegie Mellon University
Praise for the first edition:
"Markov Chains and Mixing Times is a magical book, managing to be both friendly and deep. It gently introduces probabilistic techniques so that an outsider can follow. At the same time, it is the first book covering the geometric theory of Markov chains and has much that will be new to experts. It is certainly THE book that I will use to teach from. I recommend it to all comers, an amazing achievement." - Persi Diaconis, Mary V. Sunseri Professor of Statistics and Mathematics, Stanford University
"In this book, [the authors] rapidly take a well-prepared undergraduate to the frontiers of research. Short, focused chapters with clear logical dependencies allow readers to use the book in multiple ways." - CHOICE Magazine
More details
Edition
Second Edition
Language
English
Place of publication
Providence
United States
Target group
Professional and scholarly
Edition type
New edition
Dimensions
Height: 254 mm
Width: 178 mm
Weight
945 gr
ISBN-13
978-1-4704-2962-1 (9781470429621)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Yuval Peres
Markov Chains and Mixing Times
Book
03/2017
2nd Edition
American Mathematical Society
€114.09
Article not available at the moment
Persons
David A. Levin, University of Oregon, Eugene, OR.
Yuval Peres, Microsoft Research, Redmond, WA.
Yuval Peres, Microsoft Research, Redmond, WA.
Content
Basic methods and examples: Introduction to finite Markov chains
Classical (and useful) Markov chains
Markov chain Monte Carlo: Metropolis and Glauber chains
Introduction to Markov chain mixing
Coupling
Strong stationary times
Lower bounds on mixing times
The symmetric group and shuffling cards
Random walks on networks
Hitting times
Cover times
Eigenvalues
The plot thickens: Eigenfunctions and comparison of chains
The transportation metric and path coupling
The Ising model
From shuffling cards to shuffling genes
Martingales and evolving sets
The cutoff phenomenon
Lamplighter walks
Continuous-time chains
Countable state space chains
Monotone chains
The exclusion process
Cesaro mixing time, stationary times, and hitting large sets
Coupling from the past
Open problems
Background material
Introduction to simulation
Ergodic theorem
Solutions to selected exercises
Bibliography
Notation index
Index.
Classical (and useful) Markov chains
Markov chain Monte Carlo: Metropolis and Glauber chains
Introduction to Markov chain mixing
Coupling
Strong stationary times
Lower bounds on mixing times
The symmetric group and shuffling cards
Random walks on networks
Hitting times
Cover times
Eigenvalues
The plot thickens: Eigenfunctions and comparison of chains
The transportation metric and path coupling
The Ising model
From shuffling cards to shuffling genes
Martingales and evolving sets
The cutoff phenomenon
Lamplighter walks
Continuous-time chains
Countable state space chains
Monotone chains
The exclusion process
Cesaro mixing time, stationary times, and hitting large sets
Coupling from the past
Open problems
Background material
Introduction to simulation
Ergodic theorem
Solutions to selected exercises
Bibliography
Notation index
Index.