عنوان

Probabilistic forecasting and Bayesian data assimilation /

(Number (ISBN

1107069394

(Number (ISBN

1107663911

(Number (ISBN

9781107069398

(Number (ISBN

9781107663916

Title Proper

Probabilistic forecasting and Bayesian data assimilation /

General Material Designation

[Book]

First Statement of Responsibility

Sebastian Reich, University of Potsdam and University of Reading, Colin Cotter, Imperial College, London.

Place of Publication, Distribution, etc.

Cambridge :

Name of Publisher, Distributor, etc.

Cambridge University Press,

Date of Publication, Distribution, etc.

2015.

Specific Material Designation and Extent of Item

x, 297 pages :

Other Physical Details

illustrations ;

Dimensions

26 cm

Text of Note

Includes bibliographical references and index.

Text of Note

Prologue: how to produce forecasts -- Part I: Quantifying Uncertainty -- Introduction to probability -- Computational statistics -- Stochastic processes -- Bayesian inference -- Part II: Bayesian Data Assimilation -- Basic data assimilation algorithms -- McKean approach to data assimilation -- Data assimilation for spatio-temporal processes -- Dealing with imperfect models.

0

Text of Note

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.--

Bayesian statistical decision theory.

Probabilities.

Uncertainty (Information theory)

Bayesian statistical decision theory.

Probabilities.

Uncertainty (Information theory)

Number

Edition

Class number

Book number

Reich, Sebastian.

Cotter, Colin.

Date of Transaction

20200822145801.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y