Authors:
- Provides a clear overview of the theory
- Includes exercises in each chapter
- Written by a well known author
- Includes supplementary material: sn.pub/extras
Part of the book series: Probability Theory and Stochastic Modelling (PTSM, volume 78)
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Table of contents (21 chapters)
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Front Matter
About this book
The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory.
The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book.
Reviews
“This is a book that any discrete proababilist will want to have on the shelf. It is a comprehensive extension of the author's masterfully written text Markov Chains ... Surprisingly; the book contains an extensive amount of information theory. ... In my opinion the new book would be ideal for a year-long course on discrete probability.” (Yevgeniy Kovchegov, Mathematical Reviews, May, 2018)
“This is a very carefully and well-written book. The real pleasure comes from the contents but also from the excellent fonts and layout. Graduate university students and their teachers can benefit a lot of reading and using this book. There are more than good reasons to strongly recommend the book to anybody studying, teaching and/or researching in probability and its applications.” (Jordan M. Stoyanov, zbMATH 1386.60003, 2018)
“This book is an excellent piece of writing. It has the strictness of a mathematical book whose traditional purpose is to state and prove theorems, and also has the features of a book on an engineering topic, where solved and unsolved exercises are provided. I appreciated the very carefully selected solved examples that are interwoven in each chapter. They provide an indispensable aid to digest the concepts and methods presented.” (Dimitrios Katsaros, Computing Reviews, February, 21, 2018)
“This is a comprehensive volume on the application of discrete probability to combinatorics, information theory, and related fields. It is accessible for first-year graduate students. … Results are easy to find and reasonably easy to understand. … Summing Up: Recommended. Graduate students and faculty.” (M. Bona, Choice, Vol. 54 (12), August, 2017)
Authors and Affiliations
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INRIA, École Normale Supérieure, Paris, France
Pierre Brémaud
About the author
Bibliographic Information
Book Title: Discrete Probability Models and Methods
Book Subtitle: Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding
Authors: Pierre Brémaud
Series Title: Probability Theory and Stochastic Modelling
DOI: https://doi.org/10.1007/978-3-319-43476-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2017
Hardcover ISBN: 978-3-319-43475-9Published: 03 February 2017
Softcover ISBN: 978-3-319-82835-0Published: 13 July 2018
eBook ISBN: 978-3-319-43476-6Published: 31 January 2017
Series ISSN: 2199-3130
Series E-ISSN: 2199-3149
Edition Number: 1
Number of Pages: XIV, 559
Number of Illustrations: 92 b/w illustrations
Topics: Probability Theory and Stochastic Processes, Probability and Statistics in Computer Science, Graph Theory, Coding and Information Theory, Computer Communication Networks