Skip to content
Register Sign in Wishlist

Probabilistic Forecasting and Bayesian Data Assimilation

$47.00 ( ) USD

  • Date Published: June 2015
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9781316309490

$ 47.00 USD ( )
Adobe eBook Reader

You will be taken to ebooks.com for this purchase
Buy eBook Add to wishlist

Other available formats:
Paperback, Hardback


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
  • In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.

    • Opens up the subject for non-mathematicians working in any field where Bayesian data assimilation is applied
    • Provides a novel unifying framework for ensemble-based data assimilation techniques
    • MATLAB code is available to download from www.cambridge.org/9781107069398
    Read more

    Reviews & endorsements

    "… an ideal platform for capstone experiences tailored to students with interests spanning applied mathematics and statistics."
    D. V. Feldman, Choice

    'Looking at it again from the mathematician’s viewpoint, this is a beautiful articulation of the deep fact that methods which were originally developed to solve specific problems, and to get around specific issues, can be reformulated as special instances of a general theory. This book by Reich and Cotter thus makes an important and potentially very influential contribution to the literature. It is arguably most exciting in that the perspective promises to produce more and better algorithms. What more could one ask of a mathematical theory?' Christopher Jones, SIAM Review

    See more reviews

    Customer reviews

    Not yet reviewed

    Be the first to review

    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: June 2015
    • format: Adobe eBook Reader
    • isbn: 9781316309490
    • contains: 70 b/w illus. 7 colour illus. 70 exercises
    • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • Table of Contents

    Preface
    1. Prologue: how to produce forecasts
    Part I. Quantifying Uncertainty:
    2. Introduction to probability
    3. Computational statistics
    4. Stochastic processes
    5. Bayesian inference
    Part II. Bayesian Data Assimilation:
    6. Basic data assimilation algorithms
    7. McKean approach to data assimilation
    8. Data assimilation for spatio-temporal processes
    9. Dealing with imperfect models
    References
    Index.

  • Resources for

    Probabilistic Forecasting and Bayesian Data Assimilation

    Sebastian Reich, Colin Cotter

    General Resources

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.

    Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.

    Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.

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

  • Authors

    Sebastian Reich, Universität Potsdam, Germany and University of Reading
    Sebastian Reich is Professor of Numerical Analysis at the University of Potsdam (full time) and the University of Reading (part time). He also holds an honorary visiting professorship at Imperial College London. Reich is the author of over 100 journal articles and the co-author of Simulating Hamiltonian Dynamics (Cambridge, 2005), which has received more than 600 citations. His research areas cover numerical analysis and scientific computing with applications to classical mechanics, molecular dynamics, geophysical fluid dynamics, and data assimilation. In 2003 he received the Germund Dahlquist Prize from the Society for Industrial and Applied Mathematics (SIAM) for his work on geometric integration methods.

    Colin Cotter, Imperial College London
    Colin Cotter has been a Senior Lecturer in the Department of Mathematics at Imperial College London since 2013. He has published more than 40 journal articles and three book chapters, on the design, analysis and implementation of numerical methods for numerical weather prediction, ocean forecasting and climate modelling; data assimilation; image registration; geometric mechanics and other topics in scientific computing and numerical analysis. His publications have been cited approximately 500 times. He is a key member of the Met Office/STFC/NERC-funded multi-institutional 'Gung-Ho' project which will design a next generation dynamical core for the UK weather prediction and climate forecasting system. He is also a co-investigator for the EPSRC Mathematics of Planet Earth Centre for Doctoral Training, and for the EPSRC Platform for Research in Simulation Methods (PRISM).

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

Join us online

This site uses cookies to improve your experience. Read more Close

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.
×