Book contents
- Frontmatter
- Contents
- Preface
- Part I Point Processes
- Part II Optimal Control in Discrete Time
- Part III Optimal Control in Continuous Time
- Part IV Non-Linear Filtering Theory
- 12 Non-Linear Filtering with Wiener Noise
- 13 The Conditional Density
- 14 Non-Linear Filtering with Counting-Process Observations
- 15 Filtering with k-Variate Counting-Process Observations
- Part V Applications in Financial Economics
- References
- Index of Symbols
- Subject Index
12 - Non-Linear Filtering with Wiener Noise
from Part IV - Non-Linear Filtering Theory
Published online by Cambridge University Press: 27 May 2021
Book contents
- Frontmatter
- Contents
- Preface
- Part I Point Processes
- Part II Optimal Control in Discrete Time
- Part III Optimal Control in Continuous Time
- Part IV Non-Linear Filtering Theory
- 12 Non-Linear Filtering with Wiener Noise
- 13 The Conditional Density
- 14 Non-Linear Filtering with Counting-Process Observations
- 15 Filtering with k-Variate Counting-Process Observations
- Part V Applications in Financial Economics
- References
- Index of Symbols
- Subject Index
Summary
This is the start of a part of the book devoted to non-linear filtering with Wiener and point-process observations. This chapter deals with filtering with Wiener noise and we derive the Fujisaki–Kallinapur–Kunita filtering equations. We discuss finite-dimensional filters and we derive the Kalman and Wonham filters.
- Type
- Chapter
- Information
- Point Processes and Jump DiffusionsAn Introduction with Finance Applications, pp. 127 - 140Publisher: Cambridge University PressPrint publication year: 2021