# scipy.stats.qmc.MultivariateNormalQMC

class scipy.stats.qmc.MultivariateNormalQMC(mean, cov=None, *, cov_root=None, inv_transform=True, engine=None, seed=None)

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

  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.

    seed{None, int, `numpy.random.Generator`}, optional

      If seed is None the `numpy.random.Generator` singleton is used. If seed is an int, a new `Generator` instance is used, seeded with seed. If seed is already a `Generator` instance then that instance is used.

  Examples

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

  Methods

  `fast_forward`(n) | Fast-forward the sequence by n positions.
  `random`([n]) | Draw n QMC samples from the multivariate Normal.
  `reset`() | Reset the engine to base state.

scipy.stats.qmc.MultinomialQMC.reset scipy.stats.qmc.MultivariateNormalQMC.fast_forward