[SciPy-User] Weighted kernel density estimate?

[Zachary Pincus]

> I have a probability distribution represented as a set of weighted
> samples. I'd like to visualize it. Right now I'm using the weights=
> argument to np.histogram2d to do this, which is nice. What would be
> even nicer, though, would be if I could use a KDE instead of
> histogram. But the Python KDE routines I've found in a quick google
> (like, scipy.stats.kde) don't seem to have any sort of weights
> argument. Any suggestions?
> 

There have been a couple threads on this topic:
http://mail.scipy.org/pipermail/scipy-user/2012-May/032202.html
http://mail.scipy.org/pipermail/scipy-user/2013-January/033956.html

Attached is a modification to the scipy KDE routine that handles weights (only for density estimation, and none of the other tasks like integration), based on these threads.

The code is not well tested, but seems to work. See the previous threads for some caveats...

Zach


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