Automatic Differentiation
 
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poisson_log_glm_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_POISSON_LOG_GLM_LOG_HPP
2#define STAN_MATH_PRIM_PROB_POISSON_LOG_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_x &x,
17 const T_alpha &alpha,
18 const T_beta &beta) {
19 return poisson_log_glm_lpmf<propto, T_y, T_x, T_alpha, T_beta>(y, x, alpha,
20 beta);
21}
22
26template <typename T_y, typename T_x, typename T_alpha, typename T_beta>
28 const T_y &y, const T_x &x, const T_alpha &alpha, const T_beta &beta) {
29 return poisson_log_glm_lpmf<false>(y, x, alpha, beta);
30}
31} // namespace math
32} // namespace stan
33#endif
return_type_t< T_x, T_alpha, T_beta > poisson_log_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