Automatic Differentiation
 
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◆ gamma_lpdf() [2/3]

template<bool propto, typename T_y , typename T_shape , typename T_inv_scale , require_all_not_nonscalar_prim_or_rev_kernel_expression_t< T_y, T_shape, T_inv_scale > * = nullptr>
return_type_t< T_y, T_shape, T_inv_scale > stan::math::gamma_lpdf ( const T_y &  y,
const T_shape &  alpha,
const T_inv_scale &  beta 
)

The log of a gamma density for y with the specified shape and inverse scale parameters.

Shape and inverse scale parameters must be greater than 0. y must be greater than or equal to 0.

\begin{eqnarray*} y &\sim& \mbox{\sf{Gamma}}(\alpha, \beta) \\ \log (p (y \, |\, \alpha, \beta) ) &=& \log \left( \frac{\beta^\alpha}{\Gamma(\alpha)} y^{\alpha - 1} \exp^{- \beta y} \right) \\ &=& \alpha \log(\beta) - \log(\Gamma(\alpha)) + (\alpha - 1) \log(y) - \beta y\\ & & \mathrm{where} \; y > 0 \end{eqnarray*}

Template Parameters
T_ytype of scalar
T_shapetype of shape
T_inv_scaletype of inverse scale
Parameters
yA scalar variable.
alphaShape parameter.
betaInverse scale parameter.
Exceptions
std::domain_errorif alpha is not greater than 0.
std::domain_errorif beta is not greater than 0.
std::domain_errorif y is not greater than or equal to 0.

Definition at line 52 of file gamma_lpdf.hpp.