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QMC sampling from a multivariate Normal :math:`N(\mu, \Sigma)`.
Parameters
----------
mean : array_like (d,)
The mean vector. Where ``d`` is the dimension.
cov : array_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_root : array_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_transform : bool, optional
If True, use inverse transform instead of Box-Muller. Default is True.
engine : QMCEngine, optional
Quasi-Monte Carlo engine sampler. If None, `Sobol` is used.
rng : `numpy.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``.
.. versionchanged:: 1.15.0
As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
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.
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()
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