Jones, Peter Watts

Stochastic processes: an introduction - 3rd - Boca Raton CRC Press 2018 - xiv, 255 p. - Texts in Statistical Science .


Illustrates discrete random processes through the classical gambler’s ruin problem and its variants

Covers continuous random processes, such as Poisson and general population models

Describes applications of probability to modelling problems in engineering, medicine, and biology

Uses Mathematica and R to solve both theoretical and numerical examples to produce many graphs

Provides problem solutions and Mathematica® and R programs on

Provides an introduction to Brownian motion


Based on a well-established and popular course taught by the authors over many years, Stochastic Processes: An Introduction, Third Edition, discusses the modelling and analysis of random experiments, where processes evolve over time. The text begins with a review of relevant fundamental probability. It then covers gambling problems, random walks, and Markov chains. The authors go on to discuss random processes continuous in time, including Poisson, birth and death processes, and general population models, and present an extended discussion on the analysis of associated stationary processes in queues.

The book also explores reliability and other random processes, such as branching, martingales, and simple epidemics. A new chapter describing Brownian motion, where the outcomes are continuously observed over continuous time, is included. Further applications, worked examples and problems, and biographical details have been added to this edition. Much of the text has been reworked. The appendix contains key results in probability for reference.

This concise, updated book makes the material accessible, highlighting simple applications and examples. A solutions manual with fully worked answers of all end-of-chapter problems, and Mathematica® and R programs illustrating many processes discussed in the book, can be downloaded from


Stochastic processes
Stochastic processes- Problems, exercises, etc

519.23 / J6S8

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