The degree distribution P (k) in complex networks has been obtained by maximizing the nonextensive information entropy with the three constraints: < 1 >= 1, < k >= μ and < k >= ρ, where <> denotes the average over P (k). The distribution P (k) is expressed by a generalized Gaussian (referred to as Q-Gaussian) which has a maximum at k = μ with a width proportional to √ ρ− μ2, in contrast to a co...

The α-μ distribution is a generalized fading distribution which explores nonlinearities of the wireless propagation medium. This distribution includes the other distributions such as Gamma, Nakagami, exponential, Gaussian, Rayleigh etc. This makes α-μ distribution very interesting. In this paper probability density function and performance metrics such as outage and bit error rate of α-μ distri...

In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases include Gaussian scale mixtures and Laplace scale mixtures. Furthermore, Laplace s...

This note describes estimation algorithms for generalized hyperbolic hyperbolic and nor mal inverse Gaussian distributions These distributions provide a better t to empirically observed log return distributions of nancial assets than the classical normal distributions Based on the better t to the semi heavy tails of nancial assets we can compute more realistic Value at Risk estimates The modell...

The commonly made assumption of Gaussian noise is an approximation to reality. In this paper, influence function, an analysis tool in robust statistics, is used to formulate a recursive solution for the filtering of the ARMAX process with generalized t-distribution noise. By being a superset encompassing Gaussian, uniform, t, and double exponential distributions, generalized t-distribution has ...