pymc.VonMises#
- class pymc.VonMises(name, *args, rng=None, dims=None, initval=None, observed=None, total_size=None, transform=UNSET, **kwargs)[source]#
Univariate VonMises log-likelihood.
The pdf of this distribution is
\[f(x \mid \mu, \kappa) = \frac{e^{\kappa\cos(x-\mu)}}{2\pi I_0(\kappa)}\]where \(I_0\) is the modified Bessel function of order 0.
(Source code, png, hires.png, pdf)
Support
\(x \in [-\pi, \pi]\)
Mean
\(\mu\)
Variance
\(1-\frac{I_1(\kappa)}{I_0(\kappa)}\)
- Parameters
- mutensor_like of
float
, default 0 Mean.
- kappatensor_like of
float
Concentration (frac{1}{kappa} is analogous to sigma^2).
- mutensor_like of
Methods
VonMises.__init__
(*args, **kwargs)VonMises.dist
([mu, kappa])Creates a tensor variable corresponding to the cls distribution.
VonMises.moment
(size, mu, kappa)Attributes
rv_op