Generating Exponential Random Variables

Chapter 4 : Generating Random Variables

Many of the methods in computational statistics require the ability to generate random variables from known probability distributions. This is at the heart of Monte Carlo simulation for statistical inference (Chapter 6), bootstrap and resampling methods (Chapters 6 and 7), Markov chain Monte Carlo techniques (Chapter 11), and the analysis of spatial point processes (Chapter 12). In addition, we...

Generating Random Variables and Processes

Many quantitative problems in science, engineering, and economics are nowadays solved via statistical sampling on a computer. Such Monte Carlo methods can be used in three different ways: (1) to generate random objects and processes in order to observe their behavior, (2) to estimate numerical quantities by repeated sampling, and (3) to solve complicated optimization problems through randomized...

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

in this paper, we discuss 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