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About this product
- DescriptionA unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
- Author BiographySimo Sarkka worked, from 2000 to 2010, with Nokia Ltd, Indagon Ltd and 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 IEEE.
- Author(s)Simo Sarkka
- PublisherCambridge University Press
- Date of Publication05/09/2013
- Series TitleInstitute of Mathematical Statistics Textbooks
- Series Part/Volume Number3
- Place of PublicationCambridge
- Country of PublicationUnited Kingdom
- ImprintCambridge University Press
- Content Note55 b/w illus. 60 exercises
- Weight420 g
- Width152 mm
- Height228 mm
- Spine12 mm
- Format DetailsTrade paperback (US)
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