Return MLEs for shape, location, and scale parameters from data.
MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such.
One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape parameters) and floc and fscale (for location and scale parameters, respectively).
data : array_like
args : floats, optional
kwds : floats, optional
shape, loc, scale : tuple of floats
This fit is computed by maximizing a log-likelihood function, with penalty applied for samples outside of range of the distribution. The returned answer is not guaranteed to be the globally optimal MLE, it may only be locally optimal, or the optimization may fail altogether.