Bayesian Filtering and Smoothing

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, General Computing
Cover of the book Bayesian Filtering and Smoothing by Simo Särkkä, Cambridge University Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Simo Särkkä ISBN: 9781107424333
Publisher: Cambridge University Press Publication: September 5, 2013
Imprint: Cambridge University Press Language: English
Author: Simo Särkkä
ISBN: 9781107424333
Publisher: Cambridge University Press
Publication: September 5, 2013
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Cambridge University Press

Cover of the book Crime, Shame and Reintegration by Simo Särkkä
Cover of the book Author and Audience in Vitruvius' De architectura by Simo Särkkä
Cover of the book Race, Empire and First World War Writing by Simo Särkkä
Cover of the book International Taxation of Permanent Establishments by Simo Särkkä
Cover of the book Versions of Antihumanism by Simo Särkkä
Cover of the book Upheavals of Thought by Simo Särkkä
Cover of the book Introduction to Software Testing by Simo Särkkä
Cover of the book Forest Health by Simo Särkkä
Cover of the book Roman Artisans and the Urban Economy by Simo Särkkä
Cover of the book Liquid Cell Electron Microscopy by Simo Särkkä
Cover of the book Causation and Creation in Late Antiquity by Simo Särkkä
Cover of the book Modern and Postmodern Social Theorizing by Simo Särkkä
Cover of the book The Logic of Infinity by Simo Särkkä
Cover of the book Reuse and Renovation in Roman Material Culture by Simo Särkkä
Cover of the book Gaseous Radiation Detectors by Simo Särkkä
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy