Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

Details Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

2007-05-16

arxiv.org/abs/0705.2454v1

Physics

Data Analysis, Statistics and Probability

8 pages. This paper is submitted to ICA2007

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-141916