Uncertainty Quantification with R

Bayesian Methods
2024
ISBN: 978-3-031-48207-6
Verlag: Springer Nature Switzerland

Buch, Englisch, 486 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 904 g

Reihe: International Series in Operations Research & Management Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Souza De Cursi, Eduardo

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Produktionsmanagement, Qualitätskontrolle
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz

Inhaltsverzeichnis

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

Über Autor(innen)

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.