Overview
- Very suitable for self-study
- Provides many worked examples and exercises
- Suitable for beginners; no prior knowledge of probability is needed
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Undergraduate Mathematics Series (SUMS)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation. It describes how to set up and analyse models of real-life phenomena that involve elements of chance. Motivation comes from everyday experiences of probability, such as that of a dice or cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are shared or particular events arise.
Applications include branching processes, random walks, Markov chains, queues, renewal theory, and Brownian motion. This textbook contains many worked examples and several chapters have been updated and expanded for the second edition.
Some mathematical knowledge is assumed. The reader should have the ability to work with unions, intersections and complements of sets; a good facility with calculus, including integration, sequences and series; and appreciation of the logical development of an argument. Probability Modelsis designed to aid students studying probability as part of an undergraduate course on mathematics or mathematics and statistics.
Reviews
From the reviews of the second edition:
“It should be on the desk of any university teacher and anybody studying probability and stochastic processes. … there are exercises given in the end of each chapter. Solutions or good hints are summarised and located at the end of the book. All these make the book more than useful to a wide spectrum of readers. For anybody needing a good introduction to modern probability and stochastic processes, this is the book to start with.” (Jordan M. Stoyanov, zbMATH, Vol. 1286, 2014)
“This is an introductory book on probabilistic modeling that I can recommend to any student or teacher. It is not only for probability courses, but also for general mathematics, since the proofs, definitions, and examples are so beautifully intermingled and interspersed.” (Arturo Ortiz-Tapia, Computing Reviews, November, 2013)
Authors and Affiliations
Bibliographic Information
Book Title: Probability Models
Authors: John Haigh
Series Title: Springer Undergraduate Mathematics Series
DOI: https://doi.org/10.1007/978-1-4471-5343-6
Publisher: Springer London
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag London 2013
Softcover ISBN: 978-1-4471-5342-9Published: 14 July 2013
eBook ISBN: 978-1-4471-5343-6Published: 04 July 2013
Series ISSN: 1615-2085
Series E-ISSN: 2197-4144
Edition Number: 2
Number of Pages: XII, 287
Number of Illustrations: 17 b/w illustrations
Topics: Probability Theory and Stochastic Processes, Simulation and Modeling, Probability and Statistics in Computer Science, Operations Research/Decision Theory, Mathematical Applications in Computer Science, Mathematical Applications in the Physical Sciences