[SciPy-user] guassian_kde and kernel regression
Frank Lagor
dfranci@seas.upenn....
Sun Nov 2 11:43:12 CST 2008
On Sun, Nov 2, 2008 at 3:18 AM, Robert Kern <robert.kern@gmail.com> wrote:
> On Sat, Nov 1, 2008 at 18:51, Anne Archibald <aarchiba@physics.mcgill.ca>
> wrote:
> > 2008/11/1 Frank Lagor <dfranci@seas.upenn.edu>:
> >> This question is probably for Robert Kern, because I believe the he
> wrote
> >> the gaussian_kde class in scipy.stats.kde, however I would very much
> >> appreciate a response from anyone else who could help. My question is:
> Is
> >> there currently any way to perform weighted kernel density estimation
> using
> >> the gaussian_kde class? If not, what needs to be done, and how do I
> get
> >> started?
> >>
> >> Just for clarity sake-- by weighted KDE I mean that I have more than
> just
> >> the distribution of points for the density estimate. I also have an
> >> associated probability with each point. In this case, I believe it
> becomes
> >> a regression problem and I think is referred to as kernel regression. I
> >> would very much like to use the class to perform both KDE and wKDE.
> >
> > The class does not support weights right now, but I don't think it
> > would be very difficult to add them to most parts of the code,
> > essentially just adding a "weights" optional argument. The automatic
> > covariance selection would need some rethinking; you'd need to hunt
> > down some research papers. (That method is only really appropriate for
> > unimodal distributions anyway.) But it does seem valuable.
>
> What Anne said.
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
> -- Umberto Eco
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Wonderful. Thank you both very much for your responses. I will soon get
started working on it.
Take care,
Frank
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