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Probabilistic forecasting and Bayesian data assimilation / Sebastian Reich, University of Potsdam and University of Reading, Colin Cotter, Imperial College, London

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Cambridge, United Kingdom : Cambridge University Press, 2015Description: 1 Online-Ressource (x, 297 Seiten)ISBN:
  • 9781107706804
Other title:
  • Probabilistic Forecasting & Bayesian Data Assimilation
Subject(s): Genre/Form: Additional physical formats: 9781107069398. | 9781107663916. | Erscheint auch als: Probabilistic forecasting and Bayesian data assimilation. Druck-Ausgabe Cambridge : Cambridge University Press, 2015. x, 297 SeitenDDC classification:
  • 519.2 23
MSC: MSC: *62-02 | 65-02 | 62M20 | 62-07 | 62F15 | 37N99 | 65C40 | 65C05 | 65C50 | 00A06RVK: RVK: SK 830Local classification: Lokale Notation: math 8.28LOC classification:
  • QA279.5
DOI: DOI: 10.1017/CBO9781107706804Online resources: Summary: 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 areasPPN: PPN: 883313820Package identifier: Produktsigel: ZDB-20-CTM | ZDB-20-CBO
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