pymc.Triangular

class pymc.Triangular(name, *args, rng=None, dims=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs)

Continuous Triangular log-likelihood.

The pdf of this distribution is

\[\begin{split}\begin{cases} 0 & \text{for } x < a, \\ \frac{2(x-a)}{(b-a)(c-a)} & \text{for } a \le x < c, \\[4pt] \frac{2}{b-a} & \text{for } x = c, \\[4pt] \frac{2(b-x)}{(b-a)(b-c)} & \text{for } c < x \le b, \\[4pt] 0 & \text{for } b < x. \end{cases}\end{split}\]

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

Support	\(x \in [lower, upper]\)
Mean	\(\dfrac{lower + upper + c}{3}\)
Variance	\(\dfrac{upper^2 + lower^2 +c^2 - lower*upper - lower*c - upper*c}{18}\)

Parameters

lowertensor_like of float, default 0

Lower limit.

ctensor_like of float, default 0.5

Mode.

uppertensor_like of float, default 1

Upper limit.

Methods

Triangular.__init__(*args, **kwargs)
Triangular.dist([lower, upper, c])	Creates a tensor variable corresponding to the cls distribution.
Triangular.logcdf(lower, c, upper)
Triangular.logp(lower, c, upper)
Triangular.moment(size, lower, c, upper)

Attributes

bound_args_indices
rv_op