Chi#
- class pymc_experimental.distributions.Chi(name, nu, **kwargs)[source]#
\(\chi\) log-likelihood.
The pdf of this distribution is
\[f(x \mid \nu) = \frac{x^{\nu - 1}e^{-x^2/2}}{2^{\nu/2 - 1}\Gamma(\nu/2)}\](
Source code,png,hires.png,pdf)
Support
\(x \in [0, \infty)\)
Mean
\(\sqrt{2}\frac{\Gamma((\nu + 1)/2)}{\Gamma(\nu/2)}\)
Variance
\(\nu - 2\left(\frac{\Gamma((\nu + 1)/2)}{\Gamma(\nu/2)}\right)^2\)
- Parameters:
nu (tensor_like of float) – Degrees of freedom (nu > 0).
Examples
- __init__()#
Methods
__init__()chi_dist(nu, size)dist(nu, **kwargs)
