`scipy.stats._qmc:MultinomialQMC`

source: /scipy/stats/_qmc.py :2486

Signature

   class     MultinomialQMC (    pvals  :  npt.ArrayLike    ,    n_trials  :  int | numpy.integer    ,  * ,     engine  :  scipy.stats._qmc.QMCEngine | None    =  None   ,    rng  :  int | numpy.integer | numpy.random._generator.Generator | numpy.random.mtrand.RandomState | None    =  None   ,    seed    =  None     )   →  None

Members

Summary

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.
n_trials : int: Number of trials.
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 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

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()

✓