class scipy.stats.gaussian_kde(dataset)

Representation of a kernel-density estimate using Gaussian kernels.

Parameters :

dataset : (# of dims, # of data)-array

datapoints to estimate from


d int number of dimensions
n int number of datapoints


kde.evaluate(points) array evaluate the estimated pdf on a provided set of points
kde(points) array same as kde.evaluate(points)
kde.integrate_gaussian(mean, cov) float multiply pdf with a specified Gaussian and integrate over the whole domain
kde.integrate_box_1d(low, high) float integrate pdf (1D only) between two bounds
kde.integrate_box(low_bounds, high_bounds) float integrate pdf over a rectangular space between low_bounds and high_bounds
kde.integrate_kde(other_kde) float integrate two kernel density estimates multiplied together
kde.resample(size=None) array randomly sample a dataset from the estimated pdf.
kde.covariance_factor() float computes the coefficient that multiplies the data covariance matrix to obtain the kernel covariance matrix. Set this method to kde.scotts_factor or kde.silverman_factor (or subclass to provide your own). The default is scotts_factor.

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