Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Preface
- Acknowledgements
- Part I Elements of Probability Theory
- 1 Axioms of Probability Theory
- 2 Discrete Probability Spaces
- 3 Distributions on the Real Line
- 4 Discrete Distributions
- 5 Continuous Distributions
- 6 Multivariate Distributions
- 7 Expectation and Concentration
- 8 Convergence of Random Variables
- 9 Stochastic Processes
- Part II Practical Considerations
- Part III Elements of Statistical Inference
- References
- Index
9 - Stochastic Processes
from Part I - Elements of Probability Theory
Published online by Cambridge University Press: 22 July 2022
Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Preface
- Acknowledgements
- Part I Elements of Probability Theory
- 1 Axioms of Probability Theory
- 2 Discrete Probability Spaces
- 3 Distributions on the Real Line
- 4 Discrete Distributions
- 5 Continuous Distributions
- 6 Multivariate Distributions
- 7 Expectation and Concentration
- 8 Convergence of Random Variables
- 9 Stochastic Processes
- Part II Practical Considerations
- Part III Elements of Statistical Inference
- References
- Index
Summary
Stochastic processes model experiments whose outcomes are collections of variables organized in some fashion. We focus here on Markov processes, which include random walks (think of the fortune of a person gambling on black/red at the roulette over time) and branching processes (think of the behavior of a population of an asexual species where each individual gives birth to a number of otherwise identical offsprings according to a given probability distribution) .
- Type
- Chapter
- Information
- Principles of Statistical AnalysisLearning from Randomized Experiments, pp. 113 - 124Publisher: Cambridge University PressPrint publication year: 2022