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


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