6.5.4.2.2. statsmodels.sandbox.nonparametric.kdecovclass.gaussian_kde_set_covariance¶
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class
statsmodels.sandbox.nonparametric.kdecovclass.gaussian_kde_set_covariance(dataset, covariance)[source]¶ from Anne Archibald in mailinglist: http://www.nabble.com/Width-of-the-gaussian-in-stats.kde.gaussian_kde—td19558924.html#a19558924
6.5.4.2.2.1. Methods¶
__init__(dataset, covariance) |
|
covariance_factor() |
Computes the coefficient (kde.factor) that multiplies the data covariance matrix to obtain the kernel covariance matrix. |
evaluate(points) |
Evaluate the estimated pdf on a set of points. |
integrate_box(low_bounds, high_bounds[, maxpts]) |
Computes the integral of a pdf over a rectangular interval. |
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 over the whole space. |
integrate_kde(other) |
Computes the integral of the product of this kernel density estimate with another. |
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([size]) |
Randomly sample a dataset from the estimated pdf. |
scotts_factor() |
Computes the coefficient (kde.factor) that multiplies the data covariance matrix to obtain the kernel covariance matrix. |
set_bandwidth([bw_method]) |
Compute the estimator bandwidth with given method. |
silverman_factor() |