Weighted Gaussian kernel density estimation in `python`

Question

Update: Weighted samples are now supported by scipy.stats.gaussian_kde. See here and here for details.

It is currently not possible to u

Answer 1

For univariate distributions you can use KDEUnivariate from statsmodels. It is not well documented, but the fit methods accepts a weights argument. Then you cannot use FFT. Here is an example:

import matplotlib.pyplot as plt
from statsmodels.nonparametric.kde import KDEUnivariate

kde1= KDEUnivariate(np.array([10.,10.,10.,5.]))
kde1.fit(bw=0.5)
plt.plot(kde1.support, [kde1.evaluate(xi) for xi in kde1.support],'x-')

kde1= KDEUnivariate(np.array([10.,5.]))
kde1.fit(weights=np.array([3.,1.]), 
         bw=0.5,
         fft=False)
plt.plot(kde1.support, [kde1.evaluate(xi) for xi in kde1.support], 'o-')

which produces this figure: