Bayesian Filtering and Smoothing

This book offers a comprehensive introduction to advanced filtering and smoothing techniques in a Bayesian context, ideal for graduate students.

In the second edition of Bayesian Filtering and Smoothing, readers are introduced to the advanced techniques of filtering and smoothing within a unified Bayesian framework. This text serves as an accessible resource for graduate students and advanced undergraduates, offering a comprehensive overview of state-of-the-art algorithms for non-linear state space models. The book emphasizes discrete-time models and provides a thorough exploration of optimal filtering and smoothing concepts, making it an essential guide for those looking to deepen their understanding of these methods.

The updated edition includes new chapters that delve into the construction of state space models for practical applications, the discretization of continuous-time models, and advanced Gaussian filtering techniques. It also introduces posterior linearization filtering and its associated smoothers, ensuring that readers are equipped with the latest advancements in the field. The text expands on key topics such as extended Kalman filtering and parameter estimation, providing a well-rounded education on these critical aspects of Bayesian analysis.

With numerous examples and exercises, Bayesian Filtering and Smoothing encourages readers to apply theoretical concepts in practical scenarios. The inclusion of Matlab and Python code available online allows students to implement the algorithms in their own projects, enhancing the learning experience. This practical, algorithmic approach requires only modest mathematical prerequisites, making it suitable for a broad audience eager to explore the intricacies of Bayesian methods in filtering and smoothing.