Book contents
- Frontmatter
- Dedication
- Epigraph
- Contents
- Preface
- Acknowledgements
- Expanded Note for Instructors
- Part I Concepts from Modeling, Inference, and Computing
- Part II Statistical Models
- 6 Regression Models
- 7 Mixture Models
- 8 Hidden Markov Models
- 9 State-Space Models
- 10 Continuous Time Models
- Part III Appendices
- Index
- Back Cover
9 - State-Space Models
from Part II - Statistical Models
Published online by Cambridge University Press: 17 August 2023
Book contents
- Frontmatter
- Dedication
- Epigraph
- Contents
- Preface
- Acknowledgements
- Expanded Note for Instructors
- Part I Concepts from Modeling, Inference, and Computing
- Part II Statistical Models
- 6 Regression Models
- 7 Mixture Models
- 8 Hidden Markov Models
- 9 State-Space Models
- 10 Continuous Time Models
- Part III Appendices
- Index
- Back Cover
Summary
In this chapter we extend our discussion of the previous chapter to model dynamical systems with continuous state-spaces. We present statistical formulations to model and analyze noisy trajectories that evolve in a continuous state space whose output is corrupted by noise. In particular, we place special emphasis on linear Gaussian state-space models and, within this context, present Kalman filtering theory. The theory presented herein lends itself to the exploration of tracking algorithms explored in the chapter and in an end-of-chapter project.
- Type
- Chapter
- Information
- Data Modeling for the SciencesApplications, Basics, Computations, pp. 318 - 332Publisher: Cambridge University PressPrint publication year: 2023