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.
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 calculationsSubstantial scientific or popular applications of each process with occasional statistical analysisHelpful definitions and examples are provided for each processEnd of chapter exercises cover theoretical applications and practice calculations
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.
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CRC Press Title: Stochastic Processes with R ISBN: 9781032153735 was successfully transmitted to the Library of Congress. "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.
1 Stochastic Process. Discrete-time Markov Chain 2 Random Walk 3 Poisson
Process 4 Nonhomogeneous Poisson Process 5 Compound Poisson Process 6
Conditional Poisson Process 7 Birth-and-Death Process 8 Branching Process 9
Brownian Motion
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 Bachelors 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.
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