pymc.Weibull#

class pymc.Weibull(name, *args, rng=None, dims=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs)[source]#

Weibull log-likelihood.

The pdf of this distribution is

\[f(x \mid \alpha, \beta) = \frac{\alpha x^{\alpha - 1} \exp(-(\frac{x}{\beta})^{\alpha})}{\beta^\alpha}\]

(Source code, png, hires.png, pdf)

../../../_images/pymc-Weibull-1.png

Support

\(x \in [0, \infty)\)

Mean

\(\beta \Gamma(1 + \frac{1}{\alpha})\)

Variance

\(\beta^2 \Gamma(1 + \frac{2}{\alpha} - \mu^2/\beta^2)\)

Parameters
alphafloat

Shape parameter (alpha > 0).

betafloat

Scale parameter (beta > 0).

Methods

Weibull.__init__(*args, **kwargs)

Weibull.dist(alpha, beta, *args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

Weibull.logcdf(alpha, beta)

Compute the log of the cumulative distribution function for Weibull distribution at the specified value.

Weibull.logp(alpha, beta)

Weibull.moment(size, alpha, beta)

Attributes

rv_op