Bayesian Filtering and Smoothing
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.
simo s¨arkk¨a worked, from 2000 to 2010, with Nokia Ltd., Indagon Ltd., and the
Nalco Company in various industrial research projects related to telecommunications,
positioning systems, and industrial process control. Currently, he is a Senior
Researcher with the Department of Biomedical Engineering and Computational
Science at Aalto University, Finland, and Adjunct Professor with Tampere University
of Technology and Lappeenranta University of Technology. In 2011 he was a visiting
scholar with the Signal Processing and Communications Laboratory of the Department
of Engineering at the University of Cambridge. His research interests are in state and
parameter estimation in stochastic dynamic systems and, in particular, Bayesian
methods in signal processing, machine learning, and inverse problems with
applications to brain imaging, positioning systems, computer vision, and audio
signal processing. He is a Senior Member of the IEEE.
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