generating a random sample from gamma distribution using generalized exponential distribution.

A convenient way of generating gamma random variables using generalized exponential distribution

In this paper we propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1. The new method is compared with the most popular Ahrens & Dieter method and the method proposed by Best. Like Ahrens & Dieter and Best methods our method also uses the acceptance-rejection principle. But it is observed that ...

JMASM22: A Convenient Way Of Generating Normal Random Variables Using Generalized Exponential Distribution

Generating random networks from a given distribution

Several variations are given for an algorithm that generates random networks approximately respecting the probabilities given by any likelihood function, such as from a p social network model. A novel use of the genetic algorithm is incorporated in these methods, which improves its applicability to the degenerate distributions that can arise with p models. Our approach includes a convenient way...

Bivariate generalized exponential distribution

Recently it is observed that the generalized exponential distribution can be used quite effectively to analyze lifetime data in one dimension. The main aim of this paper is to define a bivariate generalized exponential distribution so that the marginals have generalized exponential distributions. It is observed that the joint probability density function, the joint cumulative distribution funct...

On Generalized Order Statistics from Exponential Distribution

Let X be a non-negative random variable (r.v.) with cumulative distribution function (cdf) F. Suppose X(r, n,m, k), (r = 1, 2, . . . , n), m is a real number and k ≥ 1, is the rth smallest generalized order statistic. Several distributional properties of the generalized order statistics from exponential distribution are given. Some characterizations of the exponential distribution based on the ...

Voice Activity Detection Using Generalized Gamma Distribution

In this work, we model speech samples with a two-sided generalized Gamma distribution and evaluate its efficiency for voice activity detection. Using a computationally inexpensive maximum likelihood approach, we employ the Bayesian Information Criterion for identifying the phoneme boundaries in noisy speech.