Uncertainty quantification with R: bayesian methods
Cursi, Eduardo Souza de
Uncertainty quantification with R: bayesian methods - Cham Springer 2024 - viii, 486 p. - International Series in Operations Research & Management Science vol. 352 .
Table of content:
Basic Bayesian Probabilities
Eduardo Souza de Cursi
Pages 1-131
Beliefs
Eduardo Souza de Cursi
Pages 133-201
Information and Entropy
Eduardo Souza de Cursi
Pages 203-264
Maximum Entropy
Eduardo Souza de Cursi
Pages 265-320
Bayesian Inference
Eduardo Souza de Cursi
Pages 321-412
Sequential Bayesian Estimation
Eduardo Souza de Cursi
Pages 413-480
Back Matter
Pages 481-486
[https://link.springer.com/book/10.1007/978-3-031-48208-3]
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.
(https://link.springer.com/book/10.1007/978-3-031-48208-3)
9783031482076
R--Computer program language
Data visualization
001.434 / CUR
Uncertainty quantification with R: bayesian methods - Cham Springer 2024 - viii, 486 p. - International Series in Operations Research & Management Science vol. 352 .
Table of content:
Basic Bayesian Probabilities
Eduardo Souza de Cursi
Pages 1-131
Beliefs
Eduardo Souza de Cursi
Pages 133-201
Information and Entropy
Eduardo Souza de Cursi
Pages 203-264
Maximum Entropy
Eduardo Souza de Cursi
Pages 265-320
Bayesian Inference
Eduardo Souza de Cursi
Pages 321-412
Sequential Bayesian Estimation
Eduardo Souza de Cursi
Pages 413-480
Back Matter
Pages 481-486
[https://link.springer.com/book/10.1007/978-3-031-48208-3]
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.
(https://link.springer.com/book/10.1007/978-3-031-48208-3)
9783031482076
R--Computer program language
Data visualization
001.434 / CUR