Hello all, A while ago, someone asked on this list about whether it would be simple to modify scipy.stats.kde.gaussian_kde to deal with weighted data: http://mail.scipy.org/pipermail/scipy-user/2008-November/018578.html Anne and Robert assured the writer that this was pretty simple (modulo bandwidth selection), though I couldn't find any code that the original author may have generated based on that advice. I've got a problem that could (perhaps) be solved neatly with weighed KDE, so I'd like to give this a go. I assume that at a minimum, to get basic gaussian_kde.evaluate() functionality: (1) The covariance calculation would need to be replaced by a weighted-covariance calculation. (Simple enough.) (2) In evaluate(), the critical part looks like this (and a similar stanza that loops over the points instead): # if there are more points than data, so loop over data for i in range(self.n): diff = self.dataset[:, i, newaxis] - points tdiff = dot(self.inv_cov, diff) energy = sum(diff*tdiff,axis=0) / 2.0 result = result + exp(-energy) I assume that, further, the 'diff' values ought to be scaled by the weights, too. Is this all that would need to be done? (For the integration and resampling, obviously, there would be a bit of other work...) Thanks, Zach