Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Series Number 3)
ISBN-13:
9781107030657
ISBN-10:
110703065X
Edition:
1
Author:
Simo Särkkä
Publication date:
2013
Publisher:
Cambridge University Press
Format:
Hardcover
254 pages
Category:
Applied
,
Mathematics
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Book details
ISBN-13:
9781107030657
ISBN-10:
110703065X
Edition:
1
Author:
Simo Särkkä
Publication date:
2013
Publisher:
Cambridge University Press
Format:
Hardcover
254 pages
Category:
Applied
,
Mathematics
Summary
Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Series Number 3) (ISBN-13: 9781107030657 and ISBN-10: 110703065X), written by authors
Simo Särkkä, was published by Cambridge University Press in 2013.
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Description
Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying 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 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. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.
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