Automatic Differentiation
 
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inv_gamma_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_INV_GAMMA_LOG_HPP
2#define STAN_MATH_PRIM_PROB_INV_GAMMA_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
28template <bool propto, typename T_y, typename T_shape, typename T_scale>
30 const T_shape& alpha,
31 const T_scale& beta) {
32 return inv_gamma_lpdf<propto, T_y, T_shape, T_scale>(y, alpha, beta);
33}
34
38template <typename T_y, typename T_shape, typename T_scale>
40 const T_shape& alpha,
41 const T_scale& beta) {
42 return inv_gamma_lpdf<T_y, T_shape, T_scale>(y, alpha, beta);
43}
44
45} // namespace math
46} // namespace stan
47#endif
return_type_t< T_y, T_shape, T_scale > inv_gamma_log(const T_y &y, const T_shape &alpha, const T_scale &beta)
The log of an inverse gamma density for y with the specified shape and scale parameters.
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