Using Scipy Gaussian Kernel Density Estimation To Calculate CDF Inverse

The gaussian_kde function in scipy.stats has a function evaluate that can returns the value of the PDF of an input point. I'm trying to use gaussian_kde to estimate the inverse CDF

Solution 1:

You can use some python tricks for fast and memory-effective estimation of the CDF (based on this answer):

    from scipy.special import ndtr
    cdf = tuple(ndtr(np.ravel(item - kde.dataset) / kde.factor).mean()
                for item in x)

It works as fast as this answer, but has linear (len(kde.dataset)) space complexity instead of the quadratic (actually, len(kde.dataset) * len(x)) one.

All you have to do next is to use inverse approximation, for instance, from statsmodels.