Uncertainty Quantification Using R

This book PDF is perfect for those who love Electronic Books genre, written by Eduardo Souza de Cursi and published by Unknown which was released on 27 April 2024 with total hardcover pages 0. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Uncertainty Quantification Using R books below.

Uncertainty Quantification Using R
Uncertainty Quantification Using R
Author : Eduardo Souza de Cursi
File Size : 45,9 Mb
Publisher : Unknown
Language : English
Release Date : 27 April 2024
ISBN : 303117786X
Pages : 0 pages
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Uncertainty Quantification Using R by Eduardo Souza de Cursi Book PDF Summary

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. 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 uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python 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 linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning. .

Uncertainty Quantification Using R

Uncertainty Quantification Using R by Eduardo Souza de Cursi

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. 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 uncertainty quantification methods.

Get Book
Uncertainty Quantification using R

Uncertainty Quantification using R by Eduardo Souza de Cursi

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. 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 uncertainty quantification methods.

Get Book
Handbook of Uncertainty Quantification

Handbook of Uncertainty Quantification by Roger Ghanem,David Higdon,Howman Owhadi

The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (

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Uncertainty Quantification and Stochastic Modeling with Matlab

Uncertainty Quantification and Stochastic Modeling with Matlab by Eduardo Souza de Cursi,Rubens Sampaio

Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability

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Uncertainty Quantification

Uncertainty Quantification by Christian Soize

This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from

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Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling

Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling by José Eduardo Souza De Cursi

This proceedings book covers a wide range of topics related to uncertainty analysis and its application in various fields of engineering and science. It explores uncertainties in numerical simulations for soil liquefaction potential, the toughness properties of construction materials, experimental tests on cyclic liquefaction potential, and the estimation of geotechnical

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On Spatio Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory

On Spatio Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory by Fabian Guignard

The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms

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Uncertainty Quantification

Uncertainty Quantification by Ralph C. Smith

The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary

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