GaussianMeasure

class probnum.quad.GaussianMeasure(mean, cov, dim=None)

Bases: probnum.quad.IntegrationMeasure

Gaussian measure on Euclidean space with given mean and covariance.

If mean and cov are scalars but dim is larger than one, mean and cov are extended to a constant vector and diagonal matrix, respectively, of appropriate dimensions.

Parameters

• mean (Union[float, floating, ndarray]) – shape=(dim,) – Mean of the Gaussian measure.

• cov (Union[float, floating, ndarray]) – shape=(dim, dim) – Covariance matrix of the Gaussian measure.

• dim (Optional[Integral]) – Dimension of the integration domain.

Methods Summary

__call__(points)	Evaluate the density function of the integration measure.
sample(n_sample[, random_state])	Sample n_sample points from the integration measure.

Methods Documentation

__call__(points)

Evaluate the density function of the integration measure.

Parameters

points (Union[float, floating, ndarray]) – shape=(n_points,) or (n_points, dim) – Input locations.

Returns

shape=(n_points,) – Density evaluated at given locations.

Return type

density_evals

sample(n_sample, random_state=None)

Sample n_sample points from the integration measure.

Parameters

• n_sample (Integral) – Number of points to be sampled

• random_state (Union[None, int, RandomState, Generator]) – Random state of the random variable corresponding to the integration measure.

Returns

shape=(n_sample,) or (n_sample,dim) – Sampled points

Return type

points