Automatic Differentiation
 
Loading...
Searching...
No Matches
bernoulli_logit_glm_log.hpp
Go to the documentation of this file.
1#ifndef STAN_MATH_PRIM_PROB_BERNOULLI_LOGIT_GLM_LOG_HPP
2#define STAN_MATH_PRIM_PROB_BERNOULLI_LOGIT_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>
16 const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta) {
17 return bernoulli_logit_glm_lpmf<propto, T_y, T_x, T_alpha, T_beta>(
18 y, x, alpha, beta);
19}
20
24template <typename T_y, typename T_x, typename T_alpha, typename T_beta>
26 const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta) {
27 return bernoulli_logit_glm_lpmf<false>(y, x, alpha, beta);
28}
29} // namespace math
30} // namespace stan
31#endif
return_type_t< T_x, T_alpha, T_beta > bernoulli_logit_glm_log(const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta)
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