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Polynomial chaos approximation

version 1.0.0.0 (8.84 KB) by Felipe Uribe
Several 1D probability distributions are approximated using the polynomial chaos expansion method

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Updated 12 Jun 2015

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The main file 'PC_examples_1D.m' contains basic examples, in which several probability distributions are approximated using the polynomial chaos (PC) expansion. The key components of this method lie in the calculation of the orthogonal polynomials and the computation of the PC coefficients:
i). Functions to compute N-dimensional Hermite, Charlier and Jacobi polynomial are provided; extension to other types of orthogonal polynomials is straightforward.
ii). The PC coefficients are estimated using the projection method, where the integral is solved using a Gauss-Hermite quadrature. This step was only programmed for the case of 1D Hermite polynomials. Therefore, further extension to other types of orthogonal polynomials is required. An implementation of the regression method for the estimation of the PC coefficients can deal with this problem (hopefully, it will be included in a future version).

Cite As

Felipe Uribe (2022). Polynomial chaos approximation (https://www.mathworks.com/matlabcentral/fileexchange/51171-polynomial-chaos-approximation), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2014a
Compatible with any release
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