pymc.Exponential#

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

Exponential log-likelihood.

The pdf of this distribution is

\[f(x \mid \lambda) = \lambda \exp\left\{ -\lambda x \right\}\]

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

../../../_images/pymc-Exponential-1.png

Support

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

Mean

\(\dfrac{1}{\lambda}\)

Variance

\(\dfrac{1}{\lambda^2}\)

Parameters
lamtensor_like of float

Rate or inverse scale (lam > 0).

Notes

Logp calculation is defined in aeppl.logprob.

Methods

Exponential.__init__(*args, **kwargs)

Exponential.dist(lam, *args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

Exponential.logcdf(mu)

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

Exponential.moment(size, mu)

Attributes

rv_op