I would like to experiment with chaotic noise versus Gaussian
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)?
rhn A.T nicholson d.0.t C-o-M