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Applied Mathematics and Computation, 1999
The paper studies (non-Gaussian) diffusions classified as either ``hypo-diffusion'' or ``hyper-diffusion'', where the \(\beta\) order moments are of the type \(t^{\beta/\alpha}\), with \(\beta\) and \(\alpha\) belonging to \(\mathbb{R}^*_+\). The authors introduce signed measures corresponding to non-Gaussian diffusions on \(\mathbb{R}\), inspired by ...
Michèle Mastrangelo +2 more
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The paper studies (non-Gaussian) diffusions classified as either ``hypo-diffusion'' or ``hyper-diffusion'', where the \(\beta\) order moments are of the type \(t^{\beta/\alpha}\), with \(\beta\) and \(\alpha\) belonging to \(\mathbb{R}^*_+\). The authors introduce signed measures corresponding to non-Gaussian diffusions on \(\mathbb{R}\), inspired by ...
Michèle Mastrangelo +2 more
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Clustering of Gaussian distributions
2016 International Joint Conference on Neural Networks (IJCNN), 2016Clustering plays a basic role in many areas of data engineering, pattern recognition and image analysis. Gaussian Mixture Model (GMM) and Cross-Entropy Clustering (CEC) can approximate data of varied shapes by covering it with several clusters e.g. elliptical ones.
Spurek, Przemysław, Pałka, Wiesław
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The complex Double Gaussian distribution
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012We present the complex Double Gaussian distribution that describes the product of two independent, non-zero mean, complex Gaussian random variables, a doubly-infinite summation of terms. This distribution is useful in a wide array of problems. We discuss its application to blind TR detection systems by deriving the Neyman-Pearson optimal detector when ...
Nicholas O'Donoughue, José M. F. Moura
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On the Distribution of the Maximum of a Gaussian Process
Theory of Probability & Its Applications, 1987Translation from Teor. Veroyatn. Primen. 31, No.1, 134-142 (Russian) (1986; Zbl 0589.60036).
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Bivariate inverse gaussian distribution
Annals of the Institute of Statistical Mathematics, 1981A bivariate inverse Gaussian (IG) density function is constructed. Relations of the bivariate IG distribution to the normal and χ2 distributions are established. The corresponding bivariate random walk (RW) density function is obtained. The properties and behaviour of bivariate IG distribution are studied for large parametric values.
Al-Hussaini, Essam K. +1 more
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On Gaussian Product Modulus Distribution
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2011In this letter, we derive a probability density function (PDF) for a modulus of product of two complex-valued Gaussian random variables. The PDF is expressed using Modified Bessel Functions, and the probability distribution is named Gaussian Product Modulus Distribution. Some examples of expectation calculation are provided.
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Nearly Gaussian Distributions and Application
Communications in Statistics - Theory and Methods, 2010We consider the random variable X that is not Gaussian but for which X c , where c = (2k + 1)/(2j + 1) with k, j ∊ {0, 1,…}, is approximately Gaussian. A variable of this type is used to model the errors made by meteorologists when forecasting temperatures.
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Bernstein Gaussian Distributions on Groups
Theory of Probability & Its Applications, 1987See the review in Zbl 0604.60010.
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