class pymc.HurdleLogNormal(name, psi, mu=0, sigma=None, tau=None, **kwargs)[source]#

Hurdle LogNormal log-likelihood.

\[\begin{split}f(x \mid \psi, \mu, \sigma) = \left\{ \begin{array}{l} (1 - \psi) \ \text{if } x = 0 \\ \psi \frac{\text{LogNormalPDF}(x \mid \mu, \sigma))} {1 - \text{LogNormalCDF}(\epsilon \mid \mu, \sigma)} \ \text{if } x=1,2,3,\ldots \end{array} \right.\end{split}\]

where \(\epsilon\) is the machine precision.

psitensor_like of float

Expected proportion of LogNormal draws (0 < psi < 1)

mutensor_like of float, default 0

Location parameter.

sigmatensor_like of float, optional

Standard deviation. (sigma > 0). (only required if tau is not specified). Defaults to 1.

tautensor_like of float, optional

Scale parameter (tau > 0). (only required if sigma is not specified). Defaults to 1.


HurdleLogNormal.dist(psi[, mu, sigma, tau])