Automatic Differentiation
 
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beta_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_BETA_LOG_HPP
2#define STAN_MATH_PRIM_PROB_BETA_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
13template <bool propto, typename T_y, typename T_scale_succ,
14 typename T_scale_fail>
16 const T_y& y, const T_scale_succ& alpha, const T_scale_fail& beta) {
17 return beta_lpdf<propto, T_y, T_scale_succ, T_scale_fail>(y, alpha, beta);
18}
19
23template <typename T_y, typename T_scale_succ, typename T_scale_fail>
25 const T_y& y, const T_scale_succ& alpha, const T_scale_fail& beta) {
26 return beta_lpdf<T_y, T_scale_succ, T_scale_fail>(y, alpha, beta);
27}
28
29} // namespace math
30} // namespace stan
31#endif
return_type_t< T_y, T_scale_succ, T_scale_fail > beta_log(const T_y &y, const T_scale_succ &alpha, const T_scale_fail &beta)
Definition beta_log.hpp:15
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