Fri Aug 27 14:09:22 CDT 2010
On Fri, Aug 27, 2010 at 14:05, Anne Archibald
> On 27 August 2010 14:56, Robert Kern <email@example.com> wrote:
>> On Fri, Aug 27, 2010 at 13:38, <firstname.lastname@example.org> wrote:
>>> I don't think I have seen any higher dimensional kernel density
>>> estimation in python besides scipy.stats.kde. The Gaussian kde in
>>> scipy.stats is targeted to the underlying Fortran code for
>>> multivariate normal cdf.
>> Only for the "integrate over a box" functionality, which was what I
>> needed at the time but is pretty rarely required otherwise. The rest
>> is pure numpy.
> I should say, integrating over a box is something I do all the time,
> though that is partly because it is cheap in my setting. For example,
> for plotting on a grid, what you really want to do is not sample on
> the grid but produce average values over the grid cells - this way you
> never miss or exaggerate a peak. So having efficient methods to
> integrate over one box or all grid cells can be really handy.
> Unfortunately I think it is often expensive even when approximations
> are made that allow discarding sufficiently distant points.
Well okay then. :-)
"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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