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Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Series Number 3),Used
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Filtering and smoothing methods are used to produce an accurate estimate of the state of a timevarying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current stateoftheart filtering and smoothing methods in a unified Bayesian framework. Readers learn what nonlinear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how stateoftheart Bayesian parameter estimation methods can be combined with stateoftheart filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous endofchapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting handson work with the methods.
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