I would like to experiment with chaotic noise versus Gaussian noise sources. Previous threads in comp.dsp have recommend summing 12 uniform RNG's to approximate Gaussian random noise, which can be done with a reasonably small computational overhead per sample. Does anyone know of any reasonably low overhead algorithms to generate random noise samples from an approximation to fat-tailed, Pareto, or power law decay distributions (which seem to more closely model chaotic noise sources)? Thanks, rhn A.T nicholson d.0.t C-o-M
algorithms for fat-tailed random noise generators?
Started by ●April 25, 2007