This text is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before using it to study a range of randomized algorithms with important applications in optimization and other problems in computing. The book will appeal not only to mathematicians, but to students of computer science who will discover much useful material. This clear and concise introduction to the subject has numerous exercises that will help students to deepen their understanding.
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"...extremely elegant...I am sure that students will find great pleasure in using the book--and that teachers will have the same pleasure in using it to prepare a course on the subject." Mathematics of Computation
"Here Haggstrom takes the beginning student from the first definitions concerning Markov chains even beyond Propp-Wilson to its refinementss and applications, all in just a hundred or so generously detailed pages. If an undergraduate reading this book comes away saying "I should have thought of that!" then the psychological barrier between school mathematics and research will have begun to break down. Few mathematical monographs provide a comparable opportunity. General readers; lower-division undergraduates through professionals." Choice
"[This series] is generally good....The use of examples to introduce the various algorithms is especially effective and makes the text easier to read." Mathematical Reviews
"The numerous examples perfectly well illustrate the more theoretical points. I am sure that students will find great pleasure in using the book-and that teachers will have the same pleasure in using it to prepare a course on the subject." Mathematics of Computation
"Here Haggstrom takes the beginning student from the first definitions concerning Markov chains even beyond Propp-Wilson to its refinementss and applications, all in just a hundred or so generously detailed pages. If an undergraduate reading this book comes away saying "I should have thought of that!" then the psychological barrier between school mathematics and research will have begun to break down. Few mathematical monographs provide a comparable opportunity. General readers; lower-division undergraduates through professionals." Choice
"[This series] is generally good....The use of examples to introduce the various algorithms is especially effective and makes the text easier to read." Mathematical Reviews
"The numerous examples perfectly well illustrate the more theoretical points. I am sure that students will find great pleasure in using the book-and that teachers will have the same pleasure in using it to prepare a course on the subject." Mathematics of Computation
Book Description
Based on a lecture course given at Chalmers University, this book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory before applying it to study a range of randomised algorithms that have important applications in computing. This book will appeal not only to mathematicians, but to students of computer science who will find much here that appeals. This is a clear and concise introduction to this subject and the numerous exercises included will help students to deepen their understanding.
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Most Helpful Customer Reviews
8 of 8 people found the following review helpful:
4.0 out of 5 stars
Well suited for courses in Markov chains,
By Charles Ashbacher (Marion, Iowa United States) - See all my reviews (TOP 500 REVIEWER) (VINE VOICE) (HALL OF FAME REVIEWER)
This review is from: Finite Markov Chains and Algorithmic Applications (London Mathematical Society Student Texts) (Paperback)
Markov chains, a sequence of actions with a random element, can be applied to nearly any process that is composed of discrete steps. Such processes include manufacturing operations, building large structures and even visiting web sites on the Internet. This book is a set of lecture notes for a course for advanced undergraduates offered by the author. It begins with a basic introduction to probability theory and covers some applications of Markov chains that I had never thought about, such as surfing the Internet. There are exercises at the end of each section although solutions are not included. With a section on computer simulation of Markov chains, the book could also be used in a special topics course in computer science, where the emphasis is on optimization. Markov chains are a valuable tool that should receive more emphasis in the education of math and computer science students. This book can serve as a basic text for classes aimed at both types of student.
5 of 5 people found the following review helpful:
5.0 out of 5 stars
Excellent introduction,
By A Customer
This review is from: Finite Markov Chains and Algorithmic Applications (London Mathematical Society Student Texts) (Hardcover)
This is a very clearly written, succinct introduction to Markov Chains (it does not aim to be exhaustive).I think this book offers one of the most accessible and efficient routes to learning the basics about markov chain monte carlo, perfect simulation,sandwiching and simulated annealing. I enjoyed this book a lot.Better to read this a few times and do the questions, and then if you must, tackle one of the more bloated expositions.
4 of 4 people found the following review helpful:
5.0 out of 5 stars
Brilliant!,
This review is from: Finite Markov Chains and Algorithmic Applications (London Mathematical Society Student Texts) (Paperback)
This book is nothing short of brilliant. The pace is relaxed and discplined at the same time, the examples are interesting, and the coverage surprisingly extensive for its mere 124 pages. The idea to present Markov chains in the context of algorithms and applications is innovative and very useful. There is no other book of its kind.
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Inside This Book
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First Sentence:
The majority of readers will probably be best off by taking the following piece of advice: Skip this chapter! Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
following transition mechanism, randomized polynomial time approximation scheme, sandwiching idea, reversible distribution, ladder walk, random walk example, future algorithm, feasible configurations, initiation function, update function, twin run, total variation distance, failed update, random walker, stationary distribution, transition graph, randomized algorithms, annealing schedule Key Phrases - Capitalized Phrases (CAPs): (learn more)
Los Angeles, Monte Carlo, Consider Example New!
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The majority of readers will probably be best off by taking the following piece of advice: Skip this chapter! Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
following transition mechanism, randomized polynomial time approximation scheme, sandwiching idea, reversible distribution, ladder walk, random walk example, future algorithm, feasible configurations, initiation function, update function, twin run, total variation distance, failed update, random walker, stationary distribution, transition graph, randomized algorithms, annealing schedule Key Phrases - Capitalized Phrases (CAPs): (learn more)
Los Angeles, Monte Carlo, Consider Example New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 9
books:
See all 9 books this book cites
- Randomized Algorithms by Rajeev Motwani in Back Matter
- Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics) by W.R. Gilks in Back Matter
- Markov Chains (Cambridge Series in Statistical and Probabilistic Mathematics) by J. R. Norris in Back Matter
- Lectures on the Coupling Method (Dover Books on Mathematics) by Torgny Lindvall in Back Matter
- Probability: Theory and Examples (Probability: Theory & Examples) by Richard Durrett in Back Matter
See all 9 books this book cites
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