jax.scipy.stats.gaussian_kde

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

Representation of a kernel-density estimate using Gaussian kernels.

LAX-backend implementation of scipy.stats._kde.gaussian_kde().

Original docstring below.

Kernel density estimation is a way to estimate the probability density function (PDF) of a random variable in a non-parametric way. gaussian_kde works for both uni-variate and multi-variate data. It includes automatic bandwidth determination. The estimation works best for a unimodal distribution; bimodal or multi-modal distributions tend to be oversmoothed.

Parameters:

• dataset (array_like) – Datapoints to estimate from. In case of univariate data this is a 1-D array, otherwise a 2-D array with shape (# of dims, # of data).

• bw_method (str, scalar or callable, optional) – The method used to calculate the estimator bandwidth. This can be ‘scott’, ‘silverman’, a scalar constant or a callable. If a scalar, this will be used directly as kde.factor. If a callable, it should take a gaussian_kde instance as only parameter and return a scalar. If None (default), ‘scott’ is used. See Notes for more details.

• weights (array_like, optional) – weights of datapoints. This must be the same shape as dataset. If None (default), the samples are assumed to be equally weighted

References

[1]

D.W. Scott, “Multivariate Density Estimation: Theory, Practice, and Visualization”, John Wiley & Sons, New York, Chicester, 1992.

[2]

B.W. Silverman, “Density Estimation for Statistics and Data Analysis”, Vol. 26, Monographs on Statistics and Applied Probability, Chapman and Hall, London, 1986.

[3]

B.A. Turlach, “Bandwidth Selection in Kernel Density Estimation: A Review”, CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.

[4]

D.M. Bashtannyk and R.J. Hyndman, “Bandwidth selection for kernel conditional density estimation”, Computational Statistics & Data Analysis, Vol. 36, pp. 279-298, 2001.

[5]

Gray P. G., 1969, Journal of the Royal Statistical Society. Series A (General), 132, 272

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

Methods

__init__(dataset[, bw_method, weights])
evaluate(points)	Evaluate the estimated pdf on a set of points.
integrate_box(low_bounds, high_bounds[, maxpts])	This method is not implemented in the JAX interface.
integrate_box_1d(low, high)	Computes the integral of a 1D pdf between two bounds.
integrate_gaussian(mean, cov)	Multiply estimated density by a multivariate Gaussian and integrate
integrate_kde(other)	Computes the integral of the product of this kernel density estimate
logpdf(x)	Evaluate the log of the estimated pdf on a provided set of points.
pdf(x)	Evaluate the estimated pdf on a provided set of points.
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