scipy.stats.qmc.

MultinomialQMC

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

QMC sampling from a multinomial distribution.

Parameters:

pvalsarray_like (k,)

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

n_trialsint

Number of trials.

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 multinomial distribution.

Examples

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Let’s define 3 categories and for a given sample, the sum of the trials of each category is 8. The number of trials per category is determined by the pvals associated to each category. Then, we sample this distribution 64 times.

>>> import matplotlib.pyplot as plt
>>> from scipy.stats import qmc
>>> dist = qmc.MultinomialQMC(
...     pvals=[0.2, 0.4, 0.4], n_trials=10, engine=qmc.Halton(d=1)
... )
>>> sample = dist.random(64)

We can plot the sample and verify that the median of number of trials for each category is following the pvals. That would be pvals * n_trials = [2, 4, 4].

>>> fig, ax = plt.subplots()
>>> ax.yaxis.get_major_locator().set_params(integer=True)
>>> _ = ax.boxplot(sample)
>>> ax.set(xlabel="Categories", ylabel="Trials")
>>> plt.show()

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