jax.scipy.stats.gaussian_kde

class jax.scipy.stats.gaussian_kde(dataset, bw_method=None, weights=None)

Gaussian Kernel Density Estimator

JAX implementation of scipy.stats.gaussian_kde.

Parameters:

• dataset (Any) – arraylike, real-valued. Data from which to estimate the distribution. If 1D, shape is (n_data,). If 2D, shape is (n_dimensions, n_data).

• bw_method – string, scalar, or callable. Either “scott”, “silverman”, a scalar value, or a callable function which takes self as a parameter.

• weights (Any) – arraylike, optional. Weights of the same shape as the dataset.

__init__(dataset, bw_method=None, weights=None)

Methods

__init__(dataset[, bw_method, weights])
evaluate(points)	Evaluate the Gaussian KDE on the given points.
integrate_box(low_bounds, high_bounds[, maxpts])	This method is not implemented in the JAX interface.
integrate_box_1d(low, high)	Integrate the distribution over the given limits.
integrate_gaussian(mean, cov)	Integrate the distribution weighted by a Gaussian.
integrate_kde(other)	Integrate the product of two Gaussian KDE distributions.
logpdf(x)	Log probability density function
pdf(x)	Probability density function
resample(key[, shape])	Randomly sample a dataset from the estimated pdf
set_bandwidth([bw_method])	This method is not implemented in the JAX interface.
tree_flatten()
tree_unflatten(aux_data, children)

Attributes

d
n
neff
dataset
weights
covariance
inv_cov