[SciPy-user] KDE question
Stefan van der Walt
Thu Nov 15 03:46:53 CST 2007
On Tue, Nov 13, 2007 at 11:57:20AM -0600, Robert Kern wrote:
> Stefan van der Walt wrote:
> > On Sat, Nov 10, 2007 at 02:08:04AM -0600, Robert Kern wrote:
> >> David Cournapeau wrote:
> >>> I am not sure I understand exactly the problem, but if the problem is to
> >>> find a contour level of a Gaussian density, it has a closed form for any
> >>> dimension.
> >> No, the problem is to find the appropriate contour level of a kernel density
> >> estimate (with Gaussian kernels in this case). Essentially, a mixture of many
> >> Gaussians, not a single one.
> > Sounds like the kind of problem that can be solved using marching
> > squares:
> > http://www.polytech.unice.fr/~lingrand/MarchingCubes/algo.html
> This solves the already-matplotlib-solved problem of drawing the contours given
> a level. That still leaves finding the correct level. Or am I underestimating
> the potential to reformulate marching squares to solve the
> integration problem, too?
No, I don't think you are. As for the line-search, since the
different components of the mixture are available, can't we evaluate
the integral (over each component) directly, rather than working with
I come from a GMM background, so a question about KDE: is there a
component placed around each datapoint? It looks that way, since
there are no means calculated anywhere, as with gaussian mixtures.
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