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Discovering dispersion: How robust is automated model discovery for human myocardial tissue? [PDF]

open access: yesBiomech Model Mechanobiol
Martonová D   +3 more
europepmc   +1 more source
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Three Parameter Generalized Gaussian Type Distribution

Tuijin Jishu/Journal of Propulsion Technology, 2023
In this article we introduced a three parameter generalized Gaussian type distribution. The various distributional properties like probability density function, moment generating function, the central moments etc., are derived. The estimators of the parameters are also obtained through the method of moments and maximum likelihood method of estimation ...
openaire   +1 more source

Circularity and Gaussianity Detection Using the Complex Generalized Gaussian Distribution

IEEE Signal Processing Letters, 2009
Knowing the statistical properties of a complex-valued signal is important in many signal processing applications by providing the necessary information for choosing the appropriate algorithm. In this paper, we provide generalized likelihood ratio tests (GLRT), based on the complex generalized Gaussian distribution (CGGD), for detecting two important ...
M. Novey, T. Adali, A. Roy
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Distributed Parameter Estimation for Univariate Generalized Gaussian Distribution over Sensor Networks

Circuits, Systems, and Signal Processing, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liang, Chen, Wen, Fuxi, Wang, Zhongmin
openaire   +2 more sources

On a Left Truncated Generalized Gaussian Distribution

Journal of Advanced Computing, 2015
The generalized Gaussian distribution is useful in analyzing the data sets arising at places like Image processing, Signal processing, Speech recognition, Statistical Quality Control, Industrial experimentation, and Biological experiments. In this paper, a Left Truncated Generalized Gaussian distribution is introduced.
K. Anithakumari   +2 more
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Generating Nonnormal Distributions via Gaussian Mixture Models

Structural Equation Modeling: A Multidisciplinary Journal, 2020
The purpose of this paper is to (1) present a method of generating nonnormal univariate and/or uncorrelated multivariate distributions using mixture models, and (2) compare the accuracy of generati...
openaire   +1 more source

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