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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
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
1107069394
(Number (ISBN
1107663911
(Number (ISBN
9781107069398
(Number (ISBN
9781107663916

TITLE AND STATEMENT OF RESPONSIBILITY

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.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Cambridge :
Name of Publisher, Distributor, etc.
Cambridge University Press,
Date of Publication, Distribution, etc.
2015.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
x, 297 pages :
Other Physical Details
illustrations ;
Dimensions
26 cm

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references and index.

CONTENTS NOTE

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

SUMMARY OR ABSTRACT

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.--

TOPICAL NAME USED AS SUBJECT

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

DEWEY DECIMAL CLASSIFICATION

Number
519
.
2
Edition
23

LIBRARY OF CONGRESS CLASSIFICATION

Class number
QA279
.
5
Book number
.
R45
2015

PERSONAL NAME - PRIMARY RESPONSIBILITY

Reich, Sebastian.

PERSONAL NAME - ALTERNATIVE RESPONSIBILITY

Cotter, Colin.

ORIGINATING SOURCE

Date of Transaction
20200822145801.0
Cataloguing Rules (Descriptive Conventions))
rda

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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