Overview
- Contains 175 exercises including research-oriented problems about special stochastic processes not covered in traditional textbooks
- Includes detailed simulation programs of the main models
- Covers topics not typically included in traditional textbooks, allowing for readers to learn quickly on many topics, including research-oriented topics
- Includes a timeline with the main contributors since the origin of probability theory until today
- Includes supplementary material: sn.pub/extras
- Request lecturer material: sn.pub/lecturer-material
Part of the book series: Universitext (UTX)
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Table of contents (17 chapters)
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Front Matter
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Probability theory
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Front Matter
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Special models
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Front Matter
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Keywords
About this book
Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes.
The first part of the text begins with a brief review of measure theory and revisits the main concepts of probability theory, from random variables to the standard limit theorems. The second part covers traditional material on stochastic processes, including martingales, discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding applications such as the gambler’s ruin chain, branching processes, symmetric random walks, and queueing systems. The third, more research-oriented part of the text, discusses special stochastic processes of interest in physics, biology, and sociology. Additional emphasis is placed on minimal models that have been used historically to develop new mathematical techniques in the field of stochastic processes: the logistic growth process, the Wright –Fisher model, Kingman’s coalescent, percolation models, the contact process, and the voter model. Further treatment of the material explains how these special processes are connected to each other from a modeling perspective as well as their simulation capabilities in C and Matlab™.
Reviews
“Stochastic Modeling by Nicolas Lanchier is an introduction to stochastic processes accessible to advanced students and interdisciplinary scientists with a background in graduate-level real analysis. The work offers a rigorous approach to stochastic models used in social, biological and physical sciences ... . Stochastic modeling provides a link between applied research in stochastic models and the literature covering their mathematical foundations.” (Ben Dyhr, Mathematical Reviews, May, 2018)
“There is a wide spectrum of topics discussed in this book. … It is also interesting to find several classical examples with all details. … The text is so carefully written and checked, that I was unable to find a single typo. The book can be strongly recommended to those students and teachers who want to be in line with modern probability theory and its diverse applications.” (Jordan M. Stoyanov, zbMATH 1360.60002, 2017)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Stochastic Modeling
Authors: Nicolas Lanchier
Series Title: Universitext
DOI: https://doi.org/10.1007/978-3-319-50038-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Softcover ISBN: 978-3-319-50037-9Published: 09 February 2017
eBook ISBN: 978-3-319-50038-6Published: 27 January 2017
Series ISSN: 0172-5939
Series E-ISSN: 2191-6675
Edition Number: 1
Number of Pages: XIII, 303
Number of Illustrations: 57 b/w illustrations, 6 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Mathematical Modeling and Industrial Mathematics