A generalized exponential continuous random variable.
Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Any optional keyword parameters can be passed to the methods of the RV object as given below:
Parameters: | x : array_like
q : array_like
a, b, c : array_like
loc : array_like, optional
scale : array_like, optional
size : int or tuple of ints, optional
moments : str, optional
Alternatively, the object may be called (as a function) to fix the shape, location, and scale parameters returning a “frozen” continuous RV object: rv = genexpon(a, b, c, loc=0, scale=1)
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Notes
The probability density function for genexpon is:
genexpon.pdf(x, a, b, c) = (a + b * (1 - exp(-c*x))) * exp(-a*x - b*x + b/c * (1-exp(-c*x)))
for x >= 0, a, b, c > 0.
References
H.K. Ryu, “An Extension of Marshall and Olkin’s Bivariate Exponential Distribution”, Journal of the American Statistical Association, 1993.
N. Balakrishnan, “The Exponential Distribution: Theory, Methods and Applications”, Asit P. Basu.
Examples
>>> from scipy.stats import genexpon
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 1)
Calculate a few first moments:
>>> a, b, c = 9.13259764654, 16.2319566006, 3.28195526908
>>> mean, var, skew, kurt = genexpon.stats(a, b, c, moments='mvsk')
Display the probability density function (pdf):
>>> x = np.linspace(genexpon.ppf(0.01, a, b, c),
... genexpon.ppf(0.99, a, b, c), 100)
>>> ax.plot(x, genexpon.pdf(x, a, b, c),
... 'r-', lw=5, alpha=0.6, label='genexpon pdf')
Alternatively, freeze the distribution and display the frozen pdf:
>>> rv = genexpon(a, b, c)
>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
Check accuracy of cdf and ppf:
>>> vals = genexpon.ppf([0.001, 0.5, 0.999], a, b, c)
>>> np.allclose([0.001, 0.5, 0.999], genexpon.cdf(vals, a, b, c))
True
Generate random numbers:
>>> r = genexpon.rvs(a, b, c, size=1000)
And compare the histogram:
>>> ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
>>> ax.legend(loc='best', frameon=False)
>>> plt.show()
Methods
rvs(a, b, c, loc=0, scale=1, size=1) | Random variates. |
pdf(x, a, b, c, loc=0, scale=1) | Probability density function. |
logpdf(x, a, b, c, loc=0, scale=1) | Log of the probability density function. |
cdf(x, a, b, c, loc=0, scale=1) | Cumulative density function. |
logcdf(x, a, b, c, loc=0, scale=1) | Log of the cumulative density function. |
sf(x, a, b, c, loc=0, scale=1) | Survival function (1-cdf — sometimes more accurate). |
logsf(x, a, b, c, loc=0, scale=1) | Log of the survival function. |
ppf(q, a, b, c, loc=0, scale=1) | Percent point function (inverse of cdf — percentiles). |
isf(q, a, b, c, loc=0, scale=1) | Inverse survival function (inverse of sf). |
moment(n, a, b, c, loc=0, scale=1) | Non-central moment of order n |
stats(a, b, c, loc=0, scale=1, moments=’mv’) | Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’). |
entropy(a, b, c, loc=0, scale=1) | (Differential) entropy of the RV. |
fit(data, a, b, c, loc=0, scale=1) | Parameter estimates for generic data. |
expect(func, a, b, c, 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(a, b, c, loc=0, scale=1) | Median of the distribution. |
mean(a, b, c, loc=0, scale=1) | Mean of the distribution. |
var(a, b, c, loc=0, scale=1) | Variance of the distribution. |
std(a, b, c, loc=0, scale=1) | Standard deviation of the distribution. |
interval(alpha, a, b, c, loc=0, scale=1) | Endpoints of the range that contains alpha percent of the distribution |