pymc.Normal — PyMC 5.22.0 documentation (original) (raw)
class pymc.Normal(name, *args, rng=None, dims=None, initval=None, observed=None, total_size=None, transform=UNSET, default_transform=UNSET, **kwargs)[source]#
Univariate normal distribution.
The pdf of this distribution is
\[f(x \mid \mu, \tau) = \sqrt{\frac{\tau}{2\pi}} \exp\left\{ -\frac{\tau}{2} (x-\mu)^2 \right\}\]
Normal distribution can be parameterized either in terms of precision or standard deviation. The link between the two parametrizations is given by
\[\tau = \dfrac{1}{\sigma^2}\]
(Source code, png, hires.png, pdf)
| Support | \(x \in \mathbb{R}\) |
|---|---|
| Mean | \(\mu\) |
| Variance | \(\dfrac{1}{\tau}\) or \(\sigma^2\) |
Parameters:
mutensor_like of float, default 0
Mean.
sigmatensor_like of float, optional
Standard deviation (sigma > 0) (only required if tau is not specified). Defaults to 1 if neither sigma nor tau is specified.
tautensor_like of float, optional
Precision (tau > 0) (only required if sigma is not specified).
Examples
with pm.Model(): x = pm.Normal("x", mu=0, sigma=10)
with pm.Model(): x = pm.Normal("x", mu=0, tau=1 / 23)
Methods
| Normal.dist([mu, sigma, tau]) | Create a tensor variable corresponding to the cls distribution. |
|---|