Finite Markov chains and algorithmic applications /

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Bibliographic Details
Author / Creator:Häggström, Olle.
Imprint:Cambridge ; New York : Cambridge University Press, 2002.
Description:1 online resource (ix, 114 pages) : illustrations
Language:English
Series:London Mathematical Society student texts ; 52
London Mathematical Society student texts ; 52.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11119629
Hidden Bibliographic Details
ISBN:0511019416
9780511019418
9780511613586
051161358X
0521813573
0521890012
Notes:Includes bibliographical references (pages 108-112) and index.
Print version record.
Summary:Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding.
Other form:Print version: Häggström, Olle. Finite Markov chains and algorithmic applications. Cambridge ; New York : Cambridge University Press, 2002 0521813573

MARC

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300 |a 1 online resource (ix, 114 pages) :  |b illustrations 
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490 1 |a London Mathematical Society student texts ;  |v 52 
504 |a Includes bibliographical references (pages 108-112) and index. 
505 0 |a Basics of probability theory -- Markov chains -- Computer simulation of Markov chains -- Irreducible and aperiodic Markov chains -- Stationary distributions -- Reversible Markov chains -- Markov chain Monte Carlo -- Fast convergence of MCMC algorithms -- Approximate counting -- Propp-Wilson algorithm -- Sandwiching -- Propp-Wilson with read-once randomness -- Simulated annealing -- Further reading. 
588 0 |a Print version record. 
520 |a Based on a lecture course given at Chalmers University of Technology, this 2002 book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before applying it to study a range of randomized algorithms with important applications in optimization and other problems in computing. Amongst the algorithms covered are the Markov chain Monte Carlo method, simulated annealing, and the recent Propp-Wilson algorithm. This book will appeal not only to mathematicians, but also to students of statistics and computer science. The subject matter is introduced in a clear and concise fashion and the numerous exercises included will help students to deepen their understanding. 
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