Combining Polynomial Chaos Expansions and the Random Variable Transformation Technique to Approximate the Density Function of Stochastic Problems, Including Some Epidemiological Models

Combining Polynomial Chaos Expansions and the Random Variable Transformation Technique to Approximate the Density Function of Stochastic Problems, Including Some Epidemiological Models

In this paper, we deal with computational uncertainty quantification for stochastic models with one random input parameter. The goal of the paper is twofold: First, to approximate the set of probability density functions of the solution stochastic process, and second, to show the capability of our t...

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Bibliographic Details
Main Authors:	Julia Calatayud Gregori, Benito M. Chen-Charpentier, Juan Carlos Cortés López, Marc Jornet Sanz
Format:	Article
Language:	English
Published:	MDPI AG 2019-01-01
Series:	Symmetry
Subjects:	uncertainty quantification epidemiological stochastic model probability density function generalized polynomial chaos random variable transformation technique
Online Access:	http://www.mdpi.com/2073-8994/11/1/43

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