Gaussian scale mixture seems to be a good tool for modeling heavy-tailed residual in regression. I wonder if there is a systematic way to fit Gaussian scale mixture to data ? It appears that expectation-maximization algorithm only works for fitting location-scale mixture distribution, is that correct?
asked Aug 19 '11 at 14:52
Here's an answer to a slightly different question. There exists ways of more directly defining a heavy tailed distribution other than using a mixture of gaussians (which are inherently not heavy-tailed). The challenge in the past has been efficient/compact representation, as well as learning/inference. There has been some work recently to address these issues, e.g., http://www.select.cs.cmu.edu/publications/paperdir/nips10-bickson-guestrin.pdf
This might better solve whatever problem it is you have.
answered Aug 20 '11 at 14:37