Fri Aug 27 14:05:17 CDT 2010
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.
> 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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