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Uncertainty quantification using R

Cursi, Eduardo Souza de.

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Uncertainty quantification using R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Uncertainty quantification using R/ by Eduardo Souza de Cursi.
作者:	Cursi, Eduardo Souza de.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	x, 766 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Some tips to use R and RStudio -- 3. Probabilities and Random Variables -- 4. Representation of random variables -- 5. Stochastic processes -- 6. Uncertain Algebraic Equations -- 7. Random Differential Equations -- 8. UQ in Game Theory -- 9. Optimization under uncertainty -- 10. Reliability.
Contained By:	Springer Nature eBook
標題:	Uncertainty - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-3-031-17785-9
ISBN:	9783031177859

Uncertainty quantification using R
Cursi, Eduardo Souza de.

Uncertainty quantification using R[electronic resource] /by Eduardo Souza de Cursi. - Cham :Springer International Publishing :2023. - x, 766 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3352214-7934 ;. - International series in operations research & management science ;v. 335..

1. Introduction -- 2. Some tips to use R and RStudio -- 3. Probabilities and Random Variables -- 4. Representation of random variables -- 5. Stochastic processes -- 6. Uncertain Algebraic Equations -- 7. Random Differential Equations -- 8. UQ in Game Theory -- 9. Optimization under uncertainty -- 10. Reliability.

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.

ISBN: 9783031177859

Standard No.: 10.1007/978-3-031-17785-9doiSubjects--Topical Terms:

648813
Uncertainty
--Mathematical models.

LC Class. No.: QA273 / .C87 2023

Dewey Class. No.: 519.2

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520 $a This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.
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