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Non-Gaussian process generation

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A non-Gaussian random process is generated from a Gaussian-distributed white noise



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In the present files, the method to transform of a Gaussian-process into a non-Gaussian one is based on the moment-based Hermite transformation model (MBHTM), and uses a cubic transform. It has been described in [1], but I relies mainly on [2] for the implementation of the code. The non-Gaussianity is introduced by a target skewness and a target kurtosis. However, the transformation works only for a limited range for the skewness and kurtosis (see [2] for more details).
3 .m files are included:
- MBHTM.m which is the main function to generare the non-Gaussian process
- Example.m which is the example file
- fitDistEtienne.m which is used in the Example.m file. it is inspired from the matlab function fitdist.
This is the first version of the script, and therefore, some changes are excpected soon. I did not carry out anything new. All the credits goes to [1] and [2]. Any comment or proposition to improve the script is warmly welcomed !
[1] Gurley, K. R., Tognarelli, M. A., & Kareem, A. (1997). Analysis and simulation tools for wind engineering. Probabilistic Engineering Mechanics, 12(1), 9-31.
[2] Denoël, V. (2005). Application des méthodes d'analyse stochastique à l’étude des effets du vent sur les structures du génie civil. Unpublished doctoral thesis, University of Liège. (in French, p. 229)

Comments and Ratings (1)

Sevenbo Tan

Very helpful. However, thesis is in French.









-typo again



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