9781107663916-1107663911-Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics)

Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics)

ISBN-13: 9781107663916
ISBN-10: 1107663911
Edition: Reprint
Author: Reich, Sebastian, Cotter, Colin
Publication date: 2015
Publisher: Cambridge University Press
Format: Paperback 308 pages
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Book details

ISBN-13: 9781107663916
ISBN-10: 1107663911
Edition: Reprint
Author: Reich, Sebastian, Cotter, Colin
Publication date: 2015
Publisher: Cambridge University Press
Format: Paperback 308 pages

Summary

Acknowledged authors Reich, Sebastian, Cotter, Colin wrote Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics) comprising 308 pages back in 2015. Textbook and eTextbook are published under ISBN 1107663911 and 9781107663916. Since then Probabilistic Forecasting and Bayesian Data Assimilation (Cambridge Texts in Applied Mathematics) textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

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.

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