Automatic Differentiation
 
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gamma_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_GAMMA_LOG_HPP
2#define STAN_MATH_PRIM_PROB_GAMMA_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
35template <bool propto, typename T_y, typename T_shape, typename T_inv_scale>
37 const T_shape& alpha,
38 const T_inv_scale& beta) {
39 return gamma_lpdf<propto, T_y, T_shape, T_inv_scale>(y, alpha, beta);
40}
41
45template <typename T_y, typename T_shape, typename T_inv_scale>
47 const T_y& y, const T_shape& alpha, const T_inv_scale& beta) {
48 return gamma_lpdf<T_y, T_shape, T_inv_scale>(y, alpha, beta);
49}
50
51} // namespace math
52} // namespace stan
53#endif
return_type_t< T_y, T_shape, T_inv_scale > gamma_log(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.
Definition gamma_log.hpp:36
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
fvar< T > beta(const fvar< T > &x1, const fvar< T > &x2)
Return fvar with the beta function applied to the specified arguments and its gradient.
Definition beta.hpp:51
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9