Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is an ideal companion for undergraduate statistics students looking to familiarize themselves with stochastic processes before going on to more advanced courses.
Key Features
- Provides complete R codes for all simulations and calculations
- Substantial scientific or popular applications of each process with occasional statistical analysis
- Helpful definitions and examples are provided for each process
- End of chapter exercises cover theoretical applications and practice calculations
Preface
Chapter 1 Stochastic Process. Discrete-time Markov Chain
Chapter 2 Random Walk
Chapter 3 Poisson Process
Chapter 4 Nonhomogeneous Poisson Process
Chapter 5 Compound Poisson Process
Chapter 6 Conditional Poisson Process
Chapter 7 Birth-and-Death Process
Chapter 8 Branching Process
Chapter 9 Brownian Motion
Recommended books
List of Notation
Index
Biography
Olga Korosteleva, PhD, is a professor of statistics in the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She earned her Bachelor’s degree in mathematics in 1996 from Wayne State University in Detroit, and her PhD in statistics from Purdue University in West Lafayette, Indiana, in 2002. Since then she has been teaching statistics and mathematics courses at CSULB.
"This book is useful for simulating Markov chains, Poisson processes, and Brownian motion. The book can be used as supplementary reading for a first course in stochastic processes at the undergraduate-graduate level."
- David J. Olive, Technometrics, November 2022.
