cdf – Probability Distribution Functions

The module cdf offer the same functions of the module pdf but the gives the cumulative probability for the given value of x. The cumulative probability is defined like:

\int_a^x p(\xi) d\xi

where a is the inferior limit of the possible domain of the variate x. If the variate is an integer value the cumulative probability for a given integer ‘n’ is:

\sum_{i = i0}^n p_i

where i0 is the lower possible values of the random variate.

So the cdf module provides the equivalent functions of the module rnd to calculate the cumulative probability function.

cdf.gaussian(x[, sigma])
See Gaussian distribution.
cdf.exponential(x[, mu])
See Exponential Distribution.
cdf.chisq(x[, nu])
See Chi square Distribution.
cdf.laplace(x[, a])
See Laplace Distribution.
cdf.tdist(x[, nu])
See t- Distribution.
cdf.cauchy(x[, a])
See Cauchy Distribution.
cdf.rayleigh(x[, sigma])
See Rayleigh Distribution.
cdf.fdist(x, nu1, nu2)
See F- Distribution.
cdf.gamma(x, a, b)
See Gamma Distribution.
cdf.beta(x, a, b)
See Beta Distribution.
cdf.gaussian_tail(x, a, sigma)
See Gaussian tail Distribution.
cdf.exppow(x, a, b)
See Exponential Power Distribution.
cdf.lognormal(x, zeta, sigma)
See Lognormal Distribution.
cdf.binomial(x, p, n)
See Binomial Distribution.
cdf.poisson(x[, mu])
See Poisson Distribution.

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