Bayesian Filtering and Smoothing

Written for graduate and advanced undergraduate students, Bayesian Filtering and Smoothing presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. Users also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The practical and algorithmic approach of the text assumes only modest mathematical prerequisites.

Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. In addition, MATLAB code files are available for download.