scipy.stats.qmc.

MultivariateNormalQMC#

class scipy.stats.qmc.MultivariateNormalQMC(mean, cov=None, *, cov_root=None, inv_transform=True, engine=None, rng=None)[source]#

QMC sampling from a multivariate Normal \(N(\mu, \Sigma)\).

Parameters:

meanarray_like (d,): The mean vector. Where d is the dimension.
covarray_like (d, d), optional: The covariance matrix. If omitted, use cov_root instead. If both cov and cov_root are omitted, use the identity matrix.
cov_rootarray_like (d, d’), optional: A root decomposition of the covariance matrix, where d' may be less than d if the covariance is not full rank. If omitted, use cov.
inv_transformbool, optional: If True, use inverse transform instead of Box-Muller. Default is True.
engineQMCEngine, optional: Quasi-Monte Carlo engine sampler. If None, Sobol is used.
rngnumpy.random.Generator, optional: Pseudorandom number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng to instantiate a Generator.

Changed in version 1.15.0: As part of the SPEC-007 transition from use of numpy.random.RandomState to numpy.random.Generator, this keyword was changed from seed to rng. For an interim period, both keywords will continue to work, although only one may be specified at a time. After the interim period, function calls using the seed keyword will emit warnings. Following a deprecation period, the seed keyword will be removed.

Methods

random([n])

Draw n QMC samples from the multivariate Normal.

Examples

>>> import matplotlib.pyplot as plt
>>> from scipy.stats import qmc
>>> dist = qmc.MultivariateNormalQMC(mean=[0, 5], cov=[[1, 0], [0, 1]])
>>> sample = dist.random(512)
>>> _ = plt.scatter(sample[:, 0], sample[:, 1])
>>> plt.show()

../../_images/scipy-stats-qmc-MultivariateNormalQMC-1.png