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Titlebook: Uncertainty Quantification with R; Bayesian Methods Eduardo Souza de Cursi Book 2024 The Editor(s) (if applicable) and The Author(s), under

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发表于 2025-3-21 19:13:24 | 显示全部楼层 |阅读模式
书目名称Uncertainty Quantification with R
副标题Bayesian Methods
编辑Eduardo Souza de Cursi
视频video
概述Presents Bayesian techniques for uncertainty quantification.Uses R to solve complex, multivariate problems.Emphasizes practical applications of uncertainty quantification techniques for management and
丛书名称International Series in Operations Research & Management Science
图书封面Titlebook: Uncertainty Quantification with R; Bayesian Methods Eduardo Souza de Cursi Book 2024 The Editor(s) (if applicable) and The Author(s), under
描述.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 a
出版日期Book 2024
关键词uncertainty quantification; Bayesian estimation; R software; Bayesian methods; Bayesian Monte Carlo; MCMC
版次1
doihttps://doi.org/10.1007/978-3-031-48208-3
isbn_softcover978-3-031-48210-6
isbn_ebook978-3-031-48208-3Series ISSN 0884-8289 Series E-ISSN 2214-7934
issn_series 0884-8289
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Beliefs, in terms of degrees of belief. The basic notions are presented, with their implementation in R. It also explores the connections between beliefs and probabilities. Programs in R implement all the elements introduced, and their use is exemplified.
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Maximum Entropy,stochastic processes by Karhunen-Loève expansions is presented, including their combination with Hilbert’s approach of uncertainty quantification. Implementations in R are given, and their use is exemplified.
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Bayesian Inference,The choice of priors is analyzed, by using Jeffreys approach and uncertainty quantification techniques. The Expectation-Maximization Algorithm is presented in this chapter. Implementations in R are given for all the topics, with examples of use.
发表于 2025-3-22 13:08:16 | 显示全部楼层
Sequential Bayesian Estimation,ng, Particle Filtering, and Bayesian Optimization. The use of UQ for the determination of the distribution of the noise is presented. Programs in R implement all the topics introduced, with examples of use.
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0884-8289 uate 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 a978-3-031-48210-6978-3-031-48208-3Series ISSN 0884-8289 Series E-ISSN 2214-7934
发表于 2025-3-23 09:10:47 | 显示全部楼层
Book 2024ractical 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 a
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