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Stochastic Processes with R

Olga Korosteleva
€61.50
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A01=Olga Korosteleva
Author_Olga Korosteleva
branching processes
Brownian Bridge
Brownian Motion
Category=PBT
Compound Poisson
Compound Poisson Process
Continuous Time Markov Chain
Cumulative Distribution Function
Customer Arrival Process
Data Set
Discrete Time Markov Chain
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Exponentially Distributed
Geometric Brownian Motion
Homogeneous Poisson Process
Independent Exponential Times
Interarrival Times
Markov Chain
Markov processes
Negative Binomial Distribution
Nonhomogeneous Poisson Process
Poisson Distribution
Poisson Process
probability theory
R programming for stochastic processes
Random Walk
Standard Brownian Motion
statistical simulation
stochastic modeling
Symmetric Random Walk
Transition Probability Matrix
undergraduate statistics
Uniform Order Statistics
X-ray Computed Tomography

Product details

  • ISBN 9781032154732
  • Weight: 360g
  • Dimensions: 156 x 234mm
  • Publication Date: 27 May 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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
About Olga Korosteleva

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.

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