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Bayesian Filtering Smoothing by Särkkä Simo (16 results)
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Bayesian Filtering and Smoothing
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
US$ 47.52
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Bayesian Filtering and Smoothing: 17
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
Condition: New.
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Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Series Number 17)
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
Condition: New.
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Bayesian Filtering and Smoothing: 17
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
US$ 47.36
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Bayesian Filtering and Smoothing
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
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Bayesian Filtering and Smoothing: 17
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
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Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Series Number 17)
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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US$ 54.60
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Bayesian Filtering and Smoothing
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Bayesian Filtering and Smoothing
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 56.11
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US$ 50.38
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Add to basketPaperback. Condition: Brand New. 2nd edition. 430 pages. 9.00x6.00x0.89 inches. In Stock.
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Bayesian Filtering and Smoothing
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
Condition: New. 2023. 2nd Edition. Paperback. . . . . . Books ship from the US and Ireland.
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Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Band 3)
Published by Cambridge University Press, 2013
ISBN 10: 1107619289 ISBN 13: 9781107619289
Language: English
US$ 46.80
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Add to basketCondition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
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Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks, Series Number 3)
Published by Cambridge University Press, 2013
ISBN 10: 1107619289 ISBN 13: 9781107619289
Language: English
Condition: LikeNew. Text block, wraps and binding are in like new condition, without markings of any kind. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
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US$ 74.14
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Add to basketPaperback. Condition: Brand New. 2nd edition. 430 pages. 9.00x6.00x0.89 inches. In Stock.
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Bayesian Filtering and Smoothing
Published by Cambridge University Press, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
US$ 61.41
Convert currencyUS$ 57.40 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketCondition: New. Über den AutorSimo Saerkkae is Associate Professor in the Department of Electrical Engineering and Automation at Aalto University, Finland. His research interests center on state estimation and stochastic modeling, and he has authored.
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Bayesian Filtering and Smoothing
Published by Cambridge University Press Jun 2023, 2023
ISBN 10: 1108926649 ISBN 13: 9781108926645
Language: English
US$ 62.50
Convert currencyUS$ 73.95 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Neuware - 'Now in its second edition, this accessible text presents a unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with the Matlab and Python code available online, enabling readers to implement the algorithms in their own projects'--.
