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The data itself often has noise bursts or gaps, and we will
see later in Chapter that this leads us to
readjusting the weighting function. In principle, we should fix
the weighting function and solve the problem. Then we should revise
the weighting function and solve the problem again. In practice we
find it convenient to change the weighting function during the
optimization descent. Failure is possible when the weighting function
is changed too rapidly or drastically. (The proper way to solve this
problem is with robust estimators. Unfortunately, I do not yet have
an all-purpose robust solver. Thus we are (temporarily, I hope)
reduced to using crude reweighted least-squares methods. Sometimes
they work and sometimes they don't.)

** Next:** Coding nonlinear fitting problems
** Up:** THE WORLD OF CONJUGATE
** Previous:** Physical nonlinearity
Stanford Exploration Project

4/27/2004