[SciPy-user] guassian_kde and kernel regression
Sun Nov 2 02:18:22 CST 2008
On Sat, Nov 1, 2008 at 18:51, Anne Archibald <email@example.com> wrote:
> 2008/11/1 Frank Lagor <firstname.lastname@example.org>:
>> 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
>> 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.
"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
More information about the SciPy-user