Uncertainty Quantification with R
Bayesian Methods
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54 Result(s)
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Eduardo Souza de Cursi
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Chapter and Conference Paper
Process Capability Indices for Dairy Product’s Temperature Control in Dynamic Vehicle Routing
During a delivery process, and in the global transportation network chain, milk and dairy products are considered as sensible and so a higher requirement must be imposed. This paper addresses a vehicle routing...
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Chapter and Conference Paper
Isogeometric Optimization of Structural Shapes for Robustness Based on Biomimetic Principles
New challenges in shape optimization design under uncertainties lead to inspiration from nature. In this paper, we choose trees as the inspiration resource and apply the axiom of uniform strains, a governing p...
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Chapter and Conference Paper
Road Accidents Forecasting: An Uncertainty Quantification Model for Pre-disaster Management in Moroccan Context
Uncertainty quantification has become a major interest for researchers nowadays, particularly in the field of risk analysis and optimization under uncertainties. Uncertainty is an essential parameter to take i...
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Chapter and Conference Paper
Reliability-Based Design Optimization of Steel Frames Using Genetic Algorithms
In the design of structures, there are uncertainties of different origin often associated with the properties of materials, geometry and applied loads. With the Reliability-Based Design Optimization (RBDO) met...
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Book
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Chapter
Basic Bayesian Probabilities
This chapter contains a historical introduction and presents the basic elements of the Bayesian approach in probabilities, namely, the notions of exchangeability and De Finetti’s theorem. The combination of un...
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Chapter
Information and Entropy
This chapter presents the notions connected to Shannon’s entropy and information, namely the joint, conditional, relative (Kullback–Leibler) entropies, and the mutual information, with their implementations in...
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Chapter
Bayesian Inference
This chapter presents the Bayesian approach for practical tasks, such as estimation, hypothesis testing, model or variable selection, and regression. The choice of priors is analyzed, by using Jeffreys approac...
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Chapter and Conference Paper
On the Collaboration Between Bayesian and Hilbertian Approaches
In this work, we explore the use of Uncertainty Quantification (UQ) techniques of representation in Bayes estimation and representation. UQ representation is a Hilbertian approach which furnishes distributions...
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Chapter
Beliefs
This chapter presents the Dempster-Shafer theory of beliefs and plausibility, which can be seen as a formalism for the interpretation of probabilities in terms of degrees of belief. The basic notions are prese...
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Chapter
Maximum Entropy
This chapter presents the principle of maximum entropy, which furnishes a practical method for the generation of distributions. The representation of stochastic processes by Karhunen-Loève expansions is presen...
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Chapter
Sequential Bayesian Estimation
This chapter presents Monte-Carlo Markov Chain methods and connected topics, namely Importance Sampling, Metropolis-Hastings Algorithm, Kalman Filtering, Particle Filtering, and Bayesian Optimization. The use ...
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Article
Uncertainty quantification for disaster modelling: flooding as a case study
Disaster’s behaviour recognition has become an area of interest for researchers in the last decades especially with climate changes that have contributed in disaster’s severity which made their prediction more...
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Chapter
Probabilities and Random Variables
In this chapter, we present the fundamental elements of probability and statistics that are used in the book, namely the elements about random variables and random vectors, with particular attention to the use...
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Chapter
Stochastic Processes
In this chapter, we consider stochastic processes, with a focus on MA, AR, ARMA, diffusion processes, Ito’s stochastic integrals, and Ito’s stochastic differential equations.
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Chapter
Random Differential Equations
In this chapter, we examine methods for the determination of the probability distributions of random differential equations. We present also methods for the analysis of orbits and trajectories under uncertainty.
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Chapter
Optimization Under Uncertainty
This chapter presents methods for the determination of the probability distribution of the solutions of continuous optimization problems: constrained or unconstrained, linear or nonlinear. We analyze also the ...
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Book
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Chapter
Some Tips to Use R and RStudio
This chapter presents the essentials of R, with a focus on the manipulation of variables, plotting, and the use of data frames and classes. We present also some useful packages for standard numerical methods, ...
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Chapter
Representation of Random Variables
In this chapter, we present some methods to determine the distribution of a random variable from limited-sized samples. The methods are based on the representation of the random variable under consideration as...
