  Modeling non Gaussian and non stationary signals and images has always been
one of the most important part of signal and image processing methods. In this
paper, first we propose a few new models, all based on using hidden variables
for modeling either stationary but non Gaussian or Gaussian but non stationary
or non Gaussian and non stationary signals and images. Then, we will see how to
use these models in independent component analysis (ICA) or blind source
separation (BSS). The computational aspects of the Bayesian estimation
framework associated with these prior models are also discussed.
