大葉大學圖書館 |

Introduction to uncertainty quantification[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.2
書名/作者:	Introduction to uncertainty quantification/ by T.J. Sullivan.
作者:	Sullivan, T.J.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xii, 342 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Uncertainty - Mathematical models.
標題:	Mathematics.
標題:	Probability Theory and Stochastic Processes.
標題:	Optimization.
標題:	Numerical Analysis.
標題:	Appl.Mathematics/Computational Methods of Engineering.
標題:	Theoretical, Mathematical and Computational Physics.
ISBN:	9783319233956
ISBN:	9783319233949
內容註:	Introduction -- Measure and Probability Theory -- Banach and Hilbert Spaces -- Optimization Theory -- Measures of Information and Uncertainty -- Bayesian Inverse Problems -- Filtering and Data Assimilation -- Orthogonal Polynomials and Applications -- Numerical Integration -- Sensitivity Analysis and Model Reduction -- Spectral Expansions -- Stochastic Galerkin Methods -- Non-Intrusive Methods -- Distributional Uncertainty -- References -- Index.
摘要、提要註:	Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation, and numerous application areas in science and engineering. This text provides a framework in which the main objectives of the field of uncertainty quantification are defined, and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favourite problems to understand their strengths and weaknesses, also making the text suitable as a self-study. This text is designed as an introduction to uncertainty quantification for senior undergraduate and graduate students with a mathematical or statistical background, and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.
電子資源:	http://dx.doi.org/10.1007/978-3-319-23395-6

Introduction to uncertainty quantification[electronic resource] /
Sullivan, T.J.

Introduction to uncertainty quantification[electronic resource] /by T.J. Sullivan. - Cham :Springer International Publishing :2015. - xii, 342 p. :ill., digital ;24 cm. - Texts in applied mathematics,v.630939-2475 ;. - Texts in applied mathematics ;40..

Introduction -- Measure and Probability Theory -- Banach and Hilbert Spaces -- Optimization Theory -- Measures of Information and Uncertainty -- Bayesian Inverse Problems -- Filtering and Data Assimilation -- Orthogonal Polynomials and Applications -- Numerical Integration -- Sensitivity Analysis and Model Reduction -- Spectral Expansions -- Stochastic Galerkin Methods -- Non-Intrusive Methods -- Distributional Uncertainty -- References -- Index.

Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation, and numerous application areas in science and engineering. This text provides a framework in which the main objectives of the field of uncertainty quantification are defined, and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favourite problems to understand their strengths and weaknesses, also making the text suitable as a self-study. This text is designed as an introduction to uncertainty quantification for senior undergraduate and graduate students with a mathematical or statistical background, and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.

ISBN: 9783319233956

Standard No.: 10.1007/978-3-319-23395-6doiSubjects--Topical Terms:

465436
Uncertainty
--Mathematical models.

LC Class. No.: QA273

Dewey Class. No.: 519.2

Introduction to uncertainty quantification[electronic resource] /
LDR:02902nam a2200337 a 4500 001 444659
003 DE-He213
005 20160512151346.0
006 m d
007 cr nn 008maaau
008 160715s2015 gw s 0 eng d
020 $a 9783319233956 $q (electronic bk.)
020 $a 9783319233949 $q (paper)
024 7 $a 10.1007/978-3-319-23395-6 $2 doi
035 $a 978-3-319-23395-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA273
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.2 $2 23
090 $a QA273 $b .S952 2015
100 1 $a Sullivan, T.J. $3 636370
245 1 0 $a Introduction to uncertainty quantification $h [electronic resource] / $c by T.J. Sullivan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xii, 342 p. : $b ill., digital ; $c 24 cm.
490 1 $a Texts in applied mathematics, $x 0939-2475 ; $v v.63
505 0 $a Introduction -- Measure and Probability Theory -- Banach and Hilbert Spaces -- Optimization Theory -- Measures of Information and Uncertainty -- Bayesian Inverse Problems -- Filtering and Data Assimilation -- Orthogonal Polynomials and Applications -- Numerical Integration -- Sensitivity Analysis and Model Reduction -- Spectral Expansions -- Stochastic Galerkin Methods -- Non-Intrusive Methods -- Distributional Uncertainty -- References -- Index.
520 $a Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation, and numerous application areas in science and engineering. This text provides a framework in which the main objectives of the field of uncertainty quantification are defined, and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favourite problems to understand their strengths and weaknesses, also making the text suitable as a self-study. This text is designed as an introduction to uncertainty quantification for senior undergraduate and graduate students with a mathematical or statistical background, and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.
650 0 $a Uncertainty $x Mathematical models. $3 465436
650 1 4 $a Mathematics. $3 172349
650 2 4 $a Probability Theory and Stochastic Processes. $3 463894
650 2 4 $a Optimization. $3 463683
650 2 4 $a Numerical Analysis. $3 465756
650 2 4 $a Appl.Mathematics/Computational Methods of Engineering. $3 463859
650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 464132
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Texts in applied mathematics ; $v 40. $3 465731
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23395-6
950 $a Mathematics and Statistics (Springer-11649)