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عنوان
Probabilistic forecasting and Bayesian data assimilation /

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

موضوع
Bayesian statistical decision theory.,Probabilities.,Uncertainty (Information theory),Bayesian statistical decision theory.,Probabilities.,Uncertainty (Information theory)

رده
QA279
.
5
.
R45
2015

کتابخانه
کتابخانه مطالعات اسلامی به زبان های اروپایی

محل استقرار
استان: قم ـ شهر: قم

کتابخانه مطالعات اسلامی به زبان های اروپایی

تماس با کتابخانه : 32910706-025

1107069394
1107663911
9781107069398
9781107663916

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

Cambridge :
Cambridge University Press,
2015.

x, 297 pages :
illustrations ;
26 cm

Includes bibliographical references and index.

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

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)

519
.
2
23

QA279
.
5
.
R45
2015

Reich, Sebastian.

Cotter, Colin.

20200822145801.0
rda

 مطالعه متن کتاب 

[Book]

Y

الاقتراح / اعلان الخلل

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