# scipy.stats.qmc.MultinomialQMC

class scipy.stats.qmc.MultinomialQMC(pvals, *, engine=None, seed=None)

  QMC sampling from a multinomial distribution.

  Parameters
    pvals array_like (k,)

      Vector of probabilities of size `k`, where `k` is the number of categories. Elements must be non-negative and sum to 1.

    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

      >>> from scipy.stats import qmc
      >>> engine = qmc.MultinomialQMC(pvals=[0.2, 0.4, 0.4])
      >>> sample = engine.random(10)

  Methods

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

scipy.stats.qmc.LatinHypercube.reset scipy.stats.qmc.MultinomialQMC.fast_forward