An Introduction to Stochastic Modeling

by Gabriel Lord, Cónall Kelly

Overview

An Introduction to Stochastic Modeling, Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes for students familiar with elementary probability theory and calculus. The objectives are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide an integrated treatment of theory, applications and practical implementation. A well-regarded resource for many years, the text is an ideal foundation for a broad range of students.

Product Details

ISBN-13:	9780443315527
Publisher:	Elsevier Science
Publication date:	01/09/2026
Edition description:	5th ed.
Pages:	600
Product dimensions:	6.00(w) x 9.00(h) x 0.00(d)

About the Author

Gabriel J. Lord is Professor of Applied Analysis at Radboud University Nijmegen in the Netherlands since 2019. Prior to this, he was a Professor

at the Maxwell Institute in Edinburgh, UK which he joined after a couple of years in industry at the National Physical Laboratory, UK. With over

25 years teaching experience he has been giving lectures on elements of stochastic modeling for the last twenty years. He has co-authored Stochastic Methods in Neuroscience and An Introduction to Computational Stochastic PDEs. His research is in applied and computational mathematics and in particular

for stochastic systems and models.

Cónall Kelly is Associate Professor of Financial Mathematics and Director of the BSc Financial Mathematics and Actuarial Science degree at University College Cork in Ireland. He has taught courses in stochastic analysis and modeling for over 15 years and is author of the textbook Computation and Simulation for Finance: An Introduction with Python. His research is in the

qualitative dynamics of stochastic difference and differential equations, the analysis of numerical methods for stochastic systems, and applications in finance and biology.

Table of Contents

1. Introduction
2. Conditional Probability and Conditional Expectation
3. Markov Chains: Introduction
4. The Long Run Behavior of Markov Chains
5. Poisson Processes
6. Continuous Time Markov Chains
7. Renewal Phenomena
8. Queueing Systems
9. Brownian Motion and Related Processes
10. Modeling Using Stochastic Differential Equations

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From the Publisher

Classic, bestselling text for a one-semester course in stochastic processing