Skip to content
Cart

Your Cart

×

You have 0 items in your cart.

Register Sign in Wishlist

Bayesian Filtering and Smoothing

$39.99 (P)

Part of Institute of Mathematical Statistics Textbooks

  • Date Published: October 2013
  • availability: Available
  • format: Paperback
  • isbn: 9781107619289
Average user rating
(1 review)

$ 39.99 (P)
Paperback

Add to cart Add to wishlist

Other available formats:
Hardback, eBook


Looking for an examination copy?

If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • 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.

    • The first book to draw together estimation, smoothing and Monte Carlo methods
    • Examples and exercises demonstrate practical use of the algorithms
    • Matlab code is available for download, allowing readers hands-on work with the methods
    Read more

    Customer reviews

    13th Apr 2017 by Wanghs

    this is a very professional book about Bayesian filtering and Smoothing .

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: October 2013
    • format: Paperback
    • isbn: 9781107619289
    • length: 252 pages
    • dimensions: 228 x 152 x 12 mm
    • weight: 0.42kg
    • contains: 55 b/w illus. 60 exercises
    • availability: Available
  • Table of Contents

    Preface
    Symbols and abbreviations
    1. What are Bayesian filtering and smoothing?
    2. Bayesian inference
    3. Batch and recursive Bayesian estimation
    4. Bayesian filtering equations and exact solutions
    5. Extended and unscented Kalman filtering
    6. General Gaussian filtering
    7. Particle filtering
    8. Bayesian smoothing equations and exact solutions
    9. Extended and unscented smoothing
    10. General Gaussian smoothing
    11. Particle smoothing
    12. Parameter estimation
    13. Epilogue
    Appendix: additional material
    References
    Index.

  • Resources for

    Bayesian Filtering and Smoothing

    Simo Särkkä

    Welcome to the resources site

    Here you will find free-of-charge online materials to accompany this book. The range of materials we provide across our academic and higher education titles are an integral part of the book package whether you are a student, instructor, researcher or professional.

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    *This title has one or more locked files and access is given only to instructors adopting the textbook for their class. We need to enforce this strictly so that solutions are not made available to students. To gain access to locked resources you either need first to sign in or register for an account.


    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email lecturers@cambridge.org

  • Author

    Simo Särkkä, Aalto University, Finland
    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.

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×