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scipy.stats.sampling.NumericalInverseHermite.

# qrvs

NumericalInverseHermite.qrvs(size=None, d=None, qmc_engine=None)

  Quasi-random variates of the given RV.

  The qmc_engine is used to draw uniform quasi-random variates, and these are converted to quasi-random variates of the given RV using inverse transform sampling.

  Parameters:
    size int, tuple of ints, or None; optional

      Defines shape of random variates array. Default is `None`.

    d int or None, optional

      Defines dimension of uniform quasi-random variates to be transformed. Default is `None`.

    qmc_engine scipy.stats.qmc.QMCEngine(d=1), optional

      Defines the object to use for drawing quasi-random variates. Default is `None`, which uses scipy.stats.qmc.Halton(1).

  Returns:
    rvs ndarray or scalar

      Quasi-random variates. See Notes for shape information.

  Notes

  The shape of the output array depends on size, d, and qmc_engine. The intent is for the interface to be natural, but the detailed rules to achieve this are complicated.

  - If qmc_engine is `None`, a `scipy.stats.qmc.Halton` instance is created with dimension d. If d is not provided, `d=1`.

  - If qmc_engine is not `None` and d is `None`, d is determined from the dimension of the qmc_engine.

  - If qmc_engine is not `None` and d is not `None` but the dimensions are inconsistent, a `ValueError` is raised.

  - After d is determined according to the rules above, the output shape is `tuple_shape + d_shape`, where:

    > - `tuple_shape = tuple()` if size is `None`,

    > - `tuple_shape = (size,)` if size is an `int`,

    > - `tuple_shape = size` if size is a sequence,

    > - `d_shape = tuple()` if d is `None` or d is 1, and

    > - `d_shape = (d,)` if d is greater than 1.

  The elements of the returned array are part of a low-discrepancy sequence. If d is 1, this means that none of the samples are truly independent. If d > 1, each slice `rvs[..., i]` will be of a quasi-independent sequence; see `scipy.stats.qmc.QMCEngine` for details. Note that when d > 1, the samples returned are still those of the provided univariate distribution, not a multivariate generalization of that distribution.