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An Introduction to Stochastic Modeling,
Edition 5Editors: By Gabriel Lord and Cónall Kelly
Publication Date:
31 Dec 2025
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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.
Key Features
- Explores realistic applications from a variety of disciplines, including biological, chemical, physical, engineering, and financial examples
- Presents a completely new treatment of modeling with stochastic differential equations, and expanded coverage of Brownian motion and martingale processes
- New applications of Markov chains to the simulation of chemical reactions via the Gillespie algorithm and to Bayesian inference via the Metropolis-Hastings algorithm
- Provides extensive end-of-section exercises sets with answers, as well as numerical illustrations
- Each chapter concludes with a section focusing on computational examples, code, and exercises that will empower students to explore concepts in a practical way
- Offers online support, sample code and solutions to coding problems for instructors, and electronic access to sample Python code for students
About the author
By Gabriel Lord, Professor of Applied Analysis, Radboud University Nijmegen, Netherlands and Cónall Kelly, Associate Professor of Financial Mathematics and Director of the MSc Financial and Computational Mathematics, University College Cork, Ireland
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
ISBN:
9780443315527
Page Count:
600
Retail Price
:
Access to teacher/student resources is available to registered users with approved inspection copies or confirmed adoptions. To review this material, please request an inspection copy.
Upper-level undergraduate and graduate students in one-semester stochastic processes and stochastic modeling courses; assumes some background with advanced mathematics, probability theory, and calculus