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  3. Bayesian Filtering and Smoothing
  • Cited by 124
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

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