Automatic Differentiation
 
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normal_id_glm_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_NORMAL_ID_GLM_LOG_HPP
2#define STAN_MATH_PRIM_PROB_NORMAL_ID_GLM_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
13template <bool propto, typename T_y, typename T_x, typename T_alpha,
14 typename T_beta, typename T_scale>
16 const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta,
17 const T_scale &sigma) {
18 return normal_id_glm_lpdf<propto, T_y, T_x, T_alpha, T_beta, T_scale>(
19 y, x, alpha, beta, sigma);
20}
21
25template <typename T_y, typename T_x, typename T_alpha, typename T_beta,
26 typename T_scale>
28 const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta,
29 const T_scale &sigma) {
30 return normal_id_glm_lpdf<false>(y, x, alpha, beta, sigma);
31}
32} // namespace math
33} // namespace stan
34#endif
return_type_t< T_y, T_x, T_alpha, T_beta, T_scale > normal_id_glm_log(const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta, const T_scale &sigma)
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