\ABS{
The ideal regularizer is the inverse of the model covariance matrix.
Often the model covariance matrix has a complicated structure that is difficult
to characterize.  Non-stationary prediction error filters (PEF)  
have the ability to describe complicated model behavior.
Non-stationary filters are effective regularizers
for missing data and tomography problems.
}
