# Is there a closed form expression for noise other than white noise?

For white noise, where the PSD is constant, we can model that as a simple Gaussian. Is there any way to model the probability density function of other types of noise (pink noise, red noise, blue noise, etc.), and if so, is there any way to derive such a thing?

• first of all PSD and probability density function (p.d.f.) are not the same thing. "Gaussian" has nothing to do with the power spectrum. Gaussian is a property of the probability density function. you can have white noise and uniform p.d.f. or pink noise and a gaussian p.d.f. or some other p.d.f. and some completely different power spectrum. – robert bristow-johnson Mar 28 '15 at 2:00
• Right, I didn't mean to suggest that PSD and PDF are the same thing; poor wording on my part. So white/Gaussian noise can have a non-Gaussian PDF? I think that's where my confusion came from, thanks. – Andrew M Mar 28 '15 at 3:25
• p.d.f. and power spectrum are two different things. power spectrum is directly related to the autocorrelation function, and if you make an assumption of "ergodicity", then the autocorrelation function can be derived from conditional probability functions. conceptually, you can have a random process with any combination of power spectrum and p.d.f. assuming you have a good white and gaussian random number generator, there is an iterative process one can make with filtering (to get the power spectrum you want) and non-linear shaping (to get the p.d.f. you want). – robert bristow-johnson Mar 28 '15 at 4:26
• a regular uniform random number generator (like rand( )) will output a white and uniform p.d.f. random process, not gaussian. to make it approximately gaussian, you can either run the uniform through a properly-defined non-linear curve or, they usually do it by adding 12 or more independent uniform p.d.f. random numbers together. – robert bristow-johnson Mar 28 '15 at 4:29