[SciPy-user] nonlinear fit with non uniform error?
Thu Jun 21 04:29:05 CDT 2007
> > Now, if your errors are not Gaussian, least-squares is no longer the
> > correct approach and your life becomes more difficult...
> In which sense not Gaussian? In the sense that for each point, the
> uncertainity is not Gaussian distributed? It should at least with good
> approximation be. If it is in another sense, please explain...
If the error is not Gaussian (normally distributed, ...), least squares is
not the "most likely" optimization (maximizing likelyhood on gaussian data
is the same as least squares), you should use more robust cost functions.
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