ABSTRACT
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
