Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Books
  3. Bayesian Filtering and Smoothing
  • Cited by 100
Publisher:
Cambridge University Press
Online publication date:
June 2023
Print publication year:
2023
Online ISBN:
9781108917407
DOI:
https://doi.org/10.1017/9781108917407
Subjects:
Communications and Signal Processing, Engineering, Abstract Analysis, General Statistics and Probability, Applied Probability and Stochastic Networks, Statistics and Probability
Series:
Institute of Mathematical Statistics Textbooks (17)
Information

Book description

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.

Reviews

‘The book represents an excellent treatise of non-linear filtering from a Bayesian perspective. It has a nice balance between details and breadth, and it provides a nice journey from the basics of Bayesian inference to sophisticated filtering methods.’

Petar M. Djurić - Stony Brook

‘An excellent and pedagogical treatment of the complex world of nonlinear filtering.  It is very valuable for both researchers and practitioners.’

Lennart Ljung - Linköping University

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

Contents

Contents
  • 1 - What Are Bayesian Filtering and Smoothing?
    pp 1-16
      • You have access
    • PDF
    • Export citation
References

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.

Accessibility standard: Unknown

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.