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Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks Book 3) 1st Edition, Kindle Edition
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 code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.
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A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. --This text refers to the paperback edition.
About the Author
Simo Särkkä 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. --This text refers to the paperback edition.
- ASIN : B00E99YQQM
- Publisher : Cambridge University Press; 1st edition (Sept. 5 2013)
- Language : English
- File size : 11978 KB
- Simultaneous device usage : Up to 4 simultaneous devices, per publisher limits
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Sticky notes : On Kindle Scribe
- Print length : 258 pages
- Page numbers source ISBN : 1107619289
- Customer Reviews:
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4.6 out of 5
16 global ratings
Top reviews from other countries
Excellent book!Reviewed in Germany 🇩🇪 on April 12, 2014
The book is nicely written and easy to follow! It is very suitable for the those who want to learn the basics of Bayesian inference.
Good reference, but the notation is a little differentReviewed in the United States 🇺🇸 on March 5, 2014
I bought this because it is one of the few books I've seen that spells out the extended RTS filter. Nice explanation. This author has some lecture notes up on the web, and they go nicely with this book. The price is great. The only downside I found is that it could use a few COMPLETE examples. Most are set up and then graphical results given. I'd like to see a few more intermediate steps or results. Like my title says, the notation is a little different than I've seen in other books, but that shouldn't slow you down too much.
4 people found this helpful
Evandro Luiz da Costa
Outstanding reference book!Reviewed in the United States 🇺🇸 on November 13, 2013
Very comprehensive. It is a very updated book, covering most of the main methods used today. It does not go into the details, it's more like a reference book.
4 people found this helpful