
An Introduction to Stochastic Modeling
Academic Press
5th Edition
Will be published approx. on 20. January 2026
Book
Paperback/Softback
600 pages
978-0-443-31552-7 (ISBN)
Description
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.
More details
Edition
5th edition
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
College/higher education
Dimensions
Height: 226 mm
Width: 150 mm
Thickness: 25 mm
Weight
814 gr
ISBN-13
978-0-443-31552-7 (9780443315527)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Gabriel Lord | Cónall Kelly
An Introduction to Stochastic Modeling
E-Book
09/2025
5th Edition
Academic Press
€82.99
Available for download
Previous edition

Mark Pinsky | Samuel Karlin
An Introduction to Stochastic Modeling
Book
01/2011
4th Edition
Academic Press
€96.00
Shipment within 15-20 days
Persons
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.
Conall Kelly is Senior Lecturer (Associate Professor) of Financial Mathematics and Chair 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 the author of the textbook Computation and Simulation for Finance: An Introduction with Python. His research focuses on the qualitative dynamics of stochastic difference and differential equations, the analysis of numerical methods for stochastic systems, and applications in finance and biology.
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.
Conall Kelly is Senior Lecturer (Associate Professor) of Financial Mathematics and Chair 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 the author of the textbook Computation and Simulation for Finance: An Introduction with Python. His research focuses on the qualitative dynamics of stochastic difference and differential equations, the analysis of numerical methods for stochastic systems, and applications in finance and biology.
Author
Professor of Applied Analysis, Radboud University Nijmegen, Netherlands
Senior Lecturer of Financial Mathematics at University College Cork, Ireland
Content
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
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