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Uncertainty quantification with R = Bayesian methods /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Uncertainty quantification with R/ by Eduardo Souza de Cursi.
其他題名:	Bayesian methods /
作者:	Cursi, Eduardo Souza de.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	viii 486 p. :ill., digital ;24 cm.
內容註:	Introduction- 1 -- Basic Bayesian Probabilities-2 -- Beliefs-3 -- Information and Entropy-4 -- Maximum of Entropy-5 -- Bayesian Inference-6 -- Sequential Bayesian Estimation.
Contained By:	Springer Nature eBook
標題:	Uncertainty - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-3-031-48208-3
ISBN:	9783031482083

Uncertainty quantification with R = Bayesian 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. 352..

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

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

648813
Uncertainty
--Mathematical models.

LC Class. No.: QA273

Dewey Class. No.: 519.54

Uncertainty quantification with R = Bayesian methods /
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