Chaotic Method for Generating $q$-Gaussian Random Variables

Generalized Box-Muller method for generating q-Gaussian random deviates

The q-Gaussian distribution is known to be an attractor of certain correlated systems, and is the distribution which, under appropriate constraints, maximizes the entropy Sq , the basis of nonextensive statistical mechanics. This theory is postulated as a natural extension of the standard (Boltzmann-Gibbs) statistical mechanics, and may explain the ubiquitous appearance of heavy-tailed distribu...

The Ziggurat Method for Generating Random Variables

We provide a new version of our ziggurat method for generating a random variable from a given decreasing density. It is faster and simpler than the original, and will produce, for example, normal or exponential variates at the rate of 15 million per second with a C version on a 400MHz PC. It uses two tables, integers ki and reals wi. Some 99% of the time, the required x is produced by: Generate...

An Elegant Method for Generating Multivariate Poisson Random Variables

Generating multivariate Poisson data is essential in many applications. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix. We propose a computationally efficient and conceptually appealing method for generating multivariate Poisson data. The method is based on simulating multivariate Normal data an...

Recitation: Generating Random Variables for Simulation

• Usually we don’t have enough trace data to collect meaningful system measurements (statistically significant ones). • Sometimes we might purposely want to generate a Poisson arrival process, so that we can study effects of other variables. • If you’ve used empirical data to create closed-form analytic curve-fit, that analytic distribution can be very accurate. For example: the SURGE Web workl...

Generating Gaussian Pseudo-Random Deviates