[SciPy-user] Fitting an arbitrary distribution
Thu May 21 20:47:00 CDT 2009
I want to fit an arbitrary distribution (in this case the sum of multiple Gaussians) to some measured data and was wondering if anyone could give me any pointers as to the best way of doing this. I'd like to avoid fitting to a histogram if possible. How do the .fit() methods of the various distributions under scipy.stats do it? My first thought would be to compare the cumulative distribution of my data with that of the model distibution using something like the kolmogorov-smirnov metric (maximum absolute distance between the curves) and to minimize this using optimize.fmin. Is this the right way to do it? Or is there an easier way?
thanks in advance,
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