Stochastic Processes with R: An Introduction (Chapman & Hall/CRC Texts in Statistical Science)

Cover
	Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Author
1. Stochastic Process, Discrete-time Markov Chain
	1.1. Definition of Stochastic Process
	1.2. Discrete-time Markov Chain
	1.3. Chapman-Kolmogorov Equations
	1.4. Classification of States
	1.5. Limiting Probabilities
	1.6. Computations in R
	1.7. Simulations in R
	1.8. Applications of Markov Chain
	Exercises
2. Random Walk
	2.1. Definition of Random Walk
	2.2. Must-Know Facts About Random Walk
	2.3. Simulations in R
	2.4. Applications of Random Walk
	Exercises
3. Poisson Process
	3.1. Definition and Must-Know Facts About Poisson
	3.2. Simulations in R
	3.3. Applications of Poisson Process
	Exercises
4. Nonhomogeneous Poisson Process
	4.1. Definition of Nonhomogeneous Poisson Process
	4.2. Simulations in R
	4.3. Applications of Nonhomogeneous Poisson Process
	Exercises
5. Compound Poisson Process
	5.1. Definition of Compound Poisson Process
	5.2. Simulations in R
	5.3. Applications of Compound Poisson Process
	Exercises
6. Conditional Poisson Process
	6.1. Definition of Conditional Poisson Process
	6.2. Simulations in R
	6.3. Applications of Conditional Poisson Process
	Exercises
7. Birth-and-Death Process
	7.1. Definition of Birth-and-Death Process
	7.2. Simulations in R
	7.3. Applications of Birth-and-Death Process
	Exercises
8. Branching Process
	8.1. Definition of Branching Process
	8.2. Simulations in R
	8.3. Applications of Branching Process
	Exercises
9. Brownian Motion
	9.1. Definition of Brownian Motion
	9.2. Processes Derived from Brownian Motion
		9.2.1. Brownian Bridge
		9.2.2. Brownian Motion with Drift and Volatility
		9.2.3. Geometric Brownian Motion
		9.2.4. The Ornstein-Uhlenbeck Process
	9.3. Simulations in R
	9.4. Applications of Brownian Motion
	Exercises
Recommended Books
List of Notations
Index