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.
Table des matières
Introduction- 1.- Basic Bayesian Probabilities-2.- Beliefs-3.- Information and Entropy-4.- Maximum of Entropy-5.- Bayesian Inference-6.- Sequential Bayesian Estimation.
A propos de l’auteur
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 Ph D 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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