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A final problem is choosing appropriate values
for
and .
Often we have a good idea of what our data variance
is, but what we are actually adding random noise to is in
the output space of our inverse noise covariance operator.
How the variance in the data space translates to a variance
in our noise covariance operator is far from obvious.
In addition, if our value is different from the variance
of solving our estimation without noise added to the residual,
we will need to modify our value of to achieve
the same balance between our data fitting and
model styling goal.

** Next:** 1-D Super Dix
** Up:** PROBLEMS
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Stanford Exploration Project

7/8/2003