Wed Jan 21 11:17:59 CST 2009
How exactly would the EM algorithm be used? The homepage
http://pypi.python.org/pypi/scikits.learn seems to be down at the moment.
> On Wed, Jan 21, 2009 at 5:02 AM, David Trethewey <email@example.com> wrote:
>> So what I'm trying to work out now is how to use the .fit() method of
>> rv_continuous for a single gaussian and a double gaussian.
> The maximum likelihood estimator for the single gaussian is given by
> the mean and variance of your data set, but also stats.norm.fit works
> Your double gaussian is a mixture of gaussians and is not directly in
> stats distribution. I wrote a subclass for this case as an example,
> but I have to find it later, and I didn't try out the fit method.
> Fitting mixtures of gaussians can also be done (in a more
> sophisticated way) with the EM algorithm in the learn scikits package.
> One more possibility, if you are not sure about the distributional
> assumption is to use stats.kde, a gaussian kernel density estimation.
> For bimodal distributions the smoothing parameter has to be changed,
> you find some examples in this mailing list.
> I'm not sure what to use or where to find a statistical test, for the
> mixture versus unimodal distribution.
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