# scipy.stats.irwinhall#

scipy.stats.irwinhall = <scipy.stats._continuous_distns.irwinhall_gen object>[source]#

An Irwin-Hall (Uniform Sum) continuous random variable.

An Irwin-Hall continuous random variable is the sum of $$n$$ independent standard uniform random variables [1] [2].

As an instance of the rv_continuous class, irwinhall object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution.

Methods

 rvs(n, loc=0, scale=1, size=1, random_state=None) Random variates. pdf(x, n, loc=0, scale=1) Probability density function. logpdf(x, n, loc=0, scale=1) Log of the probability density function. cdf(x, n, loc=0, scale=1) Cumulative distribution function. logcdf(x, n, loc=0, scale=1) Log of the cumulative distribution function. sf(x, n, loc=0, scale=1) Survival function (also defined as 1 - cdf, but sf is sometimes more accurate). logsf(x, n, loc=0, scale=1) Log of the survival function. ppf(q, n, loc=0, scale=1) Percent point function (inverse of cdf — percentiles). isf(q, n, loc=0, scale=1) Inverse survival function (inverse of sf). moment(order, n, loc=0, scale=1) Non-central moment of the specified order. stats(n, loc=0, scale=1, moments=’mv’) Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). entropy(n, loc=0, scale=1) (Differential) entropy of the RV. fit(data) Parameter estimates for generic data. See scipy.stats.rv_continuous.fit for detailed documentation of the keyword arguments. expect(func, args=(n,), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Expected value of a function (of one argument) with respect to the distribution. median(n, loc=0, scale=1) Median of the distribution. mean(n, loc=0, scale=1) Mean of the distribution. var(n, loc=0, scale=1) Variance of the distribution. std(n, loc=0, scale=1) Standard deviation of the distribution. interval(confidence, n, loc=0, scale=1) Confidence interval with equal areas around the median.

Notes

Applications include Rao’s Spacing Test, a more powerful alternative to the Rayleigh test when the data are not unimodal, and radar [3].

Conveniently, the pdf and cdf are the $$n$$-fold convolution of the ones for the standard uniform distribution, which is also the definition of the cardinal B-splines of degree $$n-1$$ having knots evenly spaced from $$1$$ to $$n$$ [4] [5].

The Bates distribution, which represents the mean of statistically independent, uniformly distributed random variables, is simply the Irwin-Hall distribution scaled by $$1/n$$. For example, the frozen distribution bates = irwinhall(10, scale=1/10) represents the distribution of the mean of 10 uniformly distributed random variables.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, irwinhall.pdf(x, n, loc, scale) is identically equivalent to irwinhall.pdf(y, n) / scale with y = (x - loc) / scale. Note that shifting the location of a distribution does not make it a “noncentral” distribution; noncentral generalizations of some distributions are available in separate classes.

References

[1]

P. Hall, “The distribution of means for samples of size N drawn from a population in which the variate takes values between 0 and 1, all such values being equally probable”, Biometrika, Volume 19, Issue 3-4, December 1927, Pages 240-244, DOI:10.1093/biomet/19.3-4.240.

[2]

J. O. Irwin, “On the frequency distribution of the means of samples from a population having any law of frequency with finite moments, with special reference to Pearson’s Type II, Biometrika, Volume 19, Issue 3-4, December 1927, Pages 225-239, DOI:0.1093/biomet/19.3-4.225.

[3]

K. Buchanan, T. Adeyemi, C. Flores-Molina, S. Wheeland and D. Overturf, “Sidelobe behavior and bandwidth characteristics of distributed antenna arrays,” 2018 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Boulder, CO, USA, 2018, pp. 1-2. https://www.usnc-ursi-archive.org/nrsm/2018/papers/B15-9.pdf.

[4]

Amos Ron, “Lecture 1: Cardinal B-splines and convolution operators”, p. 1 https://pages.cs.wisc.edu/~deboor/887/lec1new.pdf.

[5]

Trefethen, N. (2012, July). B-splines and convolution. Chebfun. Retrieved April 30, 2024, from http://www.chebfun.org/examples/approx/BSplineConv.html.

Examples

>>> import numpy as np
>>> from scipy.stats import irwinhall
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)


Calculate the first four moments:

>>> n = 10
>>> mean, var, skew, kurt = irwinhall.stats(n, moments='mvsk')


Display the probability density function (pdf):

>>> x = np.linspace(irwinhall.ppf(0.01, n),
...                 irwinhall.ppf(0.99, n), 100)
>>> ax.plot(x, irwinhall.pdf(x, n),
...        'r-', lw=5, alpha=0.6, label='irwinhall pdf')


Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed.

Freeze the distribution and display the frozen pdf:

>>> rv = irwinhall(n)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')


Check accuracy of cdf and ppf:

>>> vals = irwinhall.ppf([0.001, 0.5, 0.999], n)
>>> np.allclose([0.001, 0.5, 0.999], irwinhall.cdf(vals, n))
True


Generate random numbers:

>>> r = irwinhall.rvs(n, size=1000)


And compare the histogram:

>>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
>>> ax.set_xlim([x[0], x[-1]])
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()