jax.scipy.stats.gaussian_kde#
- class jax.scipy.stats.gaussian_kde(dataset, bw_method=None, weights=None)[source]#
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) â 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) â The method used to calculate the estimator bandwidth. This can be âscottâ, âsilvermanâ, 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), âscottâ is used. See Notes for more details.
weights (array_like, optional) â 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, âMultivariate Density Estimation: Theory, Practice, and Visualizationâ, John Wiley & Sons, New York, Chicester, 1992.
- 2
B.W. Silverman, âDensity Estimation for Statistics and Data Analysisâ, Vol. 26, Monographs on Statistics and Applied Probability, Chapman and Hall, London, 1986.
- 3
B.A. Turlach, âBandwidth Selection in Kernel Density Estimation: A Reviewâ, CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.
- 4
D.M. Bashtannyk and R.J. Hyndman, âBandwidth selection for kernel conditional density estimationâ, 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
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