
An Introduction to Stochastic Modeling, 5th Edition
$268.99
- Paperback
600 pages
- Release Date
2 March 2026
Summary
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 processe…
Book Details
| ISBN-13: | 9780443315527 |
|---|---|
| ISBN-10: | 0443315523 |
| Author: | Gabriel Lord, Cónall Kelly |
| Publisher: | Elsevier Science Publishing Co Inc |
| Imprint: | Academic Press Inc |
| Format: | Paperback |
| Number of Pages: | 600 |
| Edition: | 5th |
| Release Date: | 2 March 2026 |
| Weight: | 810g |
| Dimensions: | 229mm x 152mm |
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About The Author
Gabriel Lord
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 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.
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