scipy.stats.qmc.

MultinomialQMC#

class scipy.stats.qmc.MultinomialQMC(pvals, n_trials, *, engine=None, seed=None)[source]#

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.

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

Used only if engine is None. If seed is an int or None, a new `numpy.random.Generator` is created using `np.random.default_rng(seed)`. If seed is already a `Generator` instance, then the provided instance is used.

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()
```

Methods

 `random`([n]) Draw n QMC samples from the multinomial distribution.