Uncertainty Quantification with R: Bayesian Methods by Eduardo Souza de Cursi
Requirements: .PDF reader, 17.4 MB
Overview: 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. This book targets the use of R, which is a GNU project to develop a tool for language and environment for statistical computing and graphics. An IDE is proposed by RStudio. The popularity of R and RStudio make that the reader will find on the web many sites and information about it. A wide literature can also be found about this software. The community of the users of R proposes a large choice of packages to extend the possibilities of R.
Genre: Non-Fiction > Tech & Devices
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Requirements: .PDF reader, 17.4 MB
Overview: 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. This book targets the use of R, which is a GNU project to develop a tool for language and environment for statistical computing and graphics. An IDE is proposed by RStudio. The popularity of R and RStudio make that the reader will find on the web many sites and information about it. A wide literature can also be found about this software. The community of the users of R proposes a large choice of packages to extend the possibilities of R.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
https://tbit.to/mp99c92vnl9o.html
https://katfile.com/uni5b1mpyzew/Uncertainty_Quantification_with_R.pdf.html
Trouble downloading? Read This.