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Uncertainty quantification with RBayesian methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncertainty quantification with Rby Eduardo Souza de Cursi.
Reminder of title:	Bayesian methods /
Author:	Cursi, Eduardo Souza de.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	viii 486 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	UncertaintyMathematical models.
Online resource:	https://doi.org/10.1007/978-3-031-48208-3
ISBN:	9783031482083$q(electronic bk.)

Uncertainty quantification with RBayesian methods /
Cursi, Eduardo Souza de.

Uncertainty quantification with RBayesian methods /[electronic resource] :by Eduardo Souza de Cursi. - Cham :Springer Nature Switzerland :2024. - viii 486 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3522214-7934 ;. - International series in operations research & management science ;v. 140..

Introduction- 1 -- Basic Bayesian Probabilities-2 -- Beliefs-3 -- Information and Entropy-4 -- Maximum of Entropy-5 -- Bayesian Inference-6 -- Sequential Bayesian Estimation.

This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. 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 Bayesian uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R 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 basic Bayesian probabilities, entropy, Bayesian estimation and decision, sequential Bayesian estimation, and numerical methods. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of Bayesian uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.

ISBN: 9783031482083$q(electronic bk.)

Standard No.: 10.1007/978-3-031-48208-3doiSubjects--Topical Terms:

263216
Uncertainty
--Mathematical models.

LC Class. No.: QA273

Dewey Class. No.: 519.54

Uncertainty quantification with RBayesian methods /
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