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    Stan Math Library
    5.1.0
    
   Automatic Differentiation 
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The log of a chi-squared density for y with the specified degrees of freedom parameter.
The degrees of freedom parameter must be greater than 0. y must be greater than or equal to 0.
\begin{eqnarray*} y &\sim& \chi^2_\nu \\ \log (p (y \, |\, \nu)) &=& \log \left( \frac{2^{-\nu / 2}}{\Gamma (\nu / 2)} y^{\nu / 2 - 1} \exp^{- y / 2} \right) \\ &=& - \frac{\nu}{2} \log(2) - \log (\Gamma (\nu / 2)) + (\frac{\nu}{2} - 1) \log(y) - \frac{y}{2} \\ & & \mathrm{ where } \; y \ge 0 \end{eqnarray*}
| T_y_cl | type of dependent variable | 
| T_dof_cl | type of degrees of freedom | 
| y | A dependent variable. | 
| nu | Degrees of freedom. | 
| std::domain_error | if nu is not greater than or equal to 0 | 
| std::domain_error | if y is not greater than or equal to 0. | 
Definition at line 38 of file chi_square_lpdf.hpp.