[SciPy-user] optimize.leastsq

David Trethewey dlrt2@ast.cam.ac...
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.

David

josef.pktd@gmail.com wrote:
> On Wed, Jan 21, 2009 at 5:02 AM, David Trethewey <dlrt2@ast.cam.ac.uk> 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.
>>
>> David
>>
>>     
>
> 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
> well.
>
> 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.
>
> Josef
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