scipy.stats.sampling.

RatioUniforms#

class scipy.stats.sampling.RatioUniforms(pdf, *, umax, vmin, vmax, c=0, random_state=None)[source]#

Generate random samples from a probability density function using the ratio-of-uniforms method.

Parameters:
pdfcallable

A function with signature pdf(x) that is proportional to the probability density function of the distribution.

umaxfloat

The upper bound of the bounding rectangle in the u-direction.

vminfloat

The lower bound of the bounding rectangle in the v-direction.

vmaxfloat

The upper bound of the bounding rectangle in the v-direction.

cfloat, optional.

Shift parameter of ratio-of-uniforms method, see Notes. Default is 0.

random_state{None, int, numpy.random.Generator,

If seed is None (or np.random), the numpy.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a Generator or RandomState instance then that instance is used.

Methods

rvs([size])

Sampling of random variates

Notes

Given a univariate probability density function pdf and a constant c, define the set A = {(u, v) : 0 < u <= sqrt(pdf(v/u + c))}. If (U, V) is a random vector uniformly distributed over A, then V/U + c follows a distribution according to pdf.

The above result (see [1], [2]) can be used to sample random variables using only the PDF, i.e. no inversion of the CDF is required. Typical choices of c are zero or the mode of pdf. The set A is a subset of the rectangle R = [0, umax] x [vmin, vmax] where

  • umax = sup sqrt(pdf(x))

  • vmin = inf (x - c) sqrt(pdf(x))

  • vmax = sup (x - c) sqrt(pdf(x))

In particular, these values are finite if pdf is bounded and x**2 * pdf(x) is bounded (i.e. subquadratic tails). One can generate (U, V) uniformly on R and return V/U + c if (U, V) are also in A which can be directly verified.

The algorithm is not changed if one replaces pdf by k * pdf for any constant k > 0. Thus, it is often convenient to work with a function that is proportional to the probability density function by dropping unnecessary normalization factors.

Intuitively, the method works well if A fills up most of the enclosing rectangle such that the probability is high that (U, V) lies in A whenever it lies in R as the number of required iterations becomes too large otherwise. To be more precise, note that the expected number of iterations to draw (U, V) uniformly distributed on R such that (U, V) is also in A is given by the ratio area(R) / area(A) = 2 * umax * (vmax - vmin) / area(pdf), where area(pdf) is the integral of pdf (which is equal to one if the probability density function is used but can take on other values if a function proportional to the density is used). The equality holds since the area of A is equal to 0.5 * area(pdf) (Theorem 7.1 in [1]). If the sampling fails to generate a single random variate after 50000 iterations (i.e. not a single draw is in A), an exception is raised.

If the bounding rectangle is not correctly specified (i.e. if it does not contain A), the algorithm samples from a distribution different from the one given by pdf. It is therefore recommended to perform a test such as kstest as a check.

References

[1] (1,2)

L. Devroye, “Non-Uniform Random Variate Generation”, Springer-Verlag, 1986.

[2]

W. Hoermann and J. Leydold, “Generating generalized inverse Gaussian random variates”, Statistics and Computing, 24(4), p. 547–557, 2014.

[3]

A.J. Kinderman and J.F. Monahan, “Computer Generation of Random Variables Using the Ratio of Uniform Deviates”, ACM Transactions on Mathematical Software, 3(3), p. 257–260, 1977.

Examples

>>> import numpy as np
>>> from scipy import stats

>>> from scipy.stats.sampling import RatioUniforms
>>> rng = np.random.default_rng()

Simulate normally distributed random variables. It is easy to compute the bounding rectangle explicitly in that case. For simplicity, we drop the normalization factor of the density.

>>> f = lambda x: np.exp(-x**2 / 2)
>>> v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
>>> umax = np.sqrt(f(0))
>>> gen = RatioUniforms(f, umax=umax, vmin=-v, vmax=v, random_state=rng)
>>> r = gen.rvs(size=2500)

The K-S test confirms that the random variates are indeed normally distributed (normality is not rejected at 5% significance level):

>>> stats.kstest(r, 'norm')[1]
0.250634764150542

The exponential distribution provides another example where the bounding rectangle can be determined explicitly.

>>> gen = RatioUniforms(lambda x: np.exp(-x), umax=1, vmin=0,
...                     vmax=2*np.exp(-1), random_state=rng)
>>> r = gen.rvs(1000)
>>> stats.kstest(r, 'expon')[1]
0.21121052054580314
On this page