Book contents
- Frontmatter
- Contents
- 1 Introduction
- Part One Consistencies
- 2 Strong Markov Consistency of Multivariate Markov Families and Processes
- 3 Consistency of Finite Multivariate Markov Chains
- 4 Consistency of Finite Multivariate Conditional Markov Chains
- 5 Consistency of Multivariate Special Semimartingales
- Part Two Structures
- Part Three Further Developments
- Part Four Applications of Stochastic Structures
- Appendices
- References
- Notation Index
- Subject Index
4 - Consistency of Finite Multivariate Conditional Markov Chains
from Part One - Consistencies
Published online by Cambridge University Press: 18 September 2020
Book contents
- Frontmatter
- Contents
- 1 Introduction
- Part One Consistencies
- 2 Strong Markov Consistency of Multivariate Markov Families and Processes
- 3 Consistency of Finite Multivariate Markov Chains
- 4 Consistency of Finite Multivariate Conditional Markov Chains
- 5 Consistency of Multivariate Special Semimartingales
- Part Two Structures
- Part Three Further Developments
- Part Four Applications of Stochastic Structures
- Appendices
- References
- Notation Index
- Subject Index
Summary
Conditional Markov Chains are an important class of stochastic processes, and thus, study of the related consistency problems is important. Finite conditional Markov chains generalize classical finite Markov chains. Thus, in many ways, the study of Markov consistency for finite multivariate conditional Markov chains done in this chapter is a generalization of the study done in Chapter 3. In particular, the results derived here are nicely illustrated by their counterparts given in the simpler set-up of Chapter 3.
- Type
- Chapter
- Information
- Structured Dependence between Stochastic Processes , pp. 49 - 66Publisher: Cambridge University PressPrint publication year: 2020