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Uncertainty Quantification with R

Bayesian Methods

  • Book
  • © 2024

Overview

Authors:
  1. Eduardo Souza de Cursi

    1. Department Mechanics / Civil Engineering, INSA Rouen Normandie, Saint-Etienne du Rouvray, France

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  • Presents Bayesian techniques for uncertainty quantification
  • Uses R to solve complex, multivariate problems
  • Emphasizes practical applications of uncertainty quantification techniques for management and planning

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 352)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-viii
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  2. Basic Bayesian Probabilities

    • Eduardo Souza de Cursi
    Pages 1-131

  3. Beliefs

    • Eduardo Souza de Cursi
    Pages 133-201

  4. Information and Entropy

    • Eduardo Souza de Cursi
    Pages 203-264

  5. Maximum Entropy

    • Eduardo Souza de Cursi
    Pages 265-320

  6. Bayesian Inference

    • Eduardo Souza de Cursi
    Pages 321-412

  7. Sequential Bayesian Estimation

    • Eduardo Souza de Cursi
    Pages 413-480

  8. Back Matter

    Pages 481-486
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Keywords

About this book

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.

Authors and Affiliations

  • Department Mechanics / Civil Engineering, INSA Rouen Normandie, Saint-Etienne du Rouvray, France

    Eduardo Souza de Cursi

About the author

Eduardo Souza De Cursi is a professor at the National Institute for Applied Sciences (INSA) in Rouen, France, where he serves as Dean of International Affairs and Director of the Laboratory of Mechanics of Normandy. He is also the Editor-in-Chief of "Computational and Applied Mathematics", a journal of the Brazilian Society of Computational and Applied Mathematics that is published with Springer. Prof. De Cursi holds a PhD in Sciences/Mathematics from the Université Des Sciences et Techniques Du Languedoc, USTL, France, and has over 35 years’ experience in research, teaching and technology transfer.

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