Automatic Differentiation
 
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categorical_logit_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_CATEGORICAL_LOGIT_LOG_HPP
2#define STAN_MATH_PRIM_PROB_CATEGORICAL_LOGIT_LOG_HPP
3
7#include <vector>
8
9namespace stan {
10namespace math {
11
15template <bool propto, typename T_n, typename T_prob>
17 const T_prob& beta) {
18 return categorical_logit_lpmf<propto>(ns, beta);
19}
20
24template <typename T_n, typename T_prob>
26 const T_prob& beta) {
27 return categorical_logit_lpmf(ns, beta);
28}
29
30} // namespace math
31} // namespace stan
32#endif
return_type_t< T_prob > categorical_logit_log(const T_n &ns, const T_prob &beta)
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
return_type_t< T_prob > categorical_logit_lpmf(int n, const T_prob &beta)
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