Stan Math Library  2.20.0
reverse mode automatic differentiation
beta_log.hpp
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1 #ifndef STAN_MATH_PRIM_SCAL_PROB_BETA_LOG_HPP
2 #define STAN_MATH_PRIM_SCAL_PROB_BETA_LOG_HPP
3 
6 
7 namespace stan {
8 namespace math {
9 
30 template <bool propto, typename T_y, typename T_scale_succ,
31  typename T_scale_fail>
33  const T_y& y, const T_scale_succ& alpha, const T_scale_fail& beta) {
34  return beta_lpdf<propto, T_y, T_scale_succ, T_scale_fail>(y, alpha, beta);
35 }
36 
40 template <typename T_y, typename T_scale_succ, typename T_scale_fail>
42  const T_y& y, const T_scale_succ& alpha, const T_scale_fail& beta) {
43  return beta_lpdf<T_y, T_scale_succ, T_scale_fail>(y, alpha, beta);
44 }
45 
46 } // namespace math
47 } // namespace stan
48 #endif
return_type< T_y, T_scale_succ, T_scale_fail >::type beta_log(const T_y &y, const T_scale_succ &alpha, const T_scale_fail &beta)
The log of the beta density for the specified scalar(s) given the specified sample size(s)...
Definition: beta_log.hpp:32
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
boost::math::tools::promote_args< double, typename scalar_type< T >::type, typename return_type< Types_pack... >::type >::type type
Definition: return_type.hpp:36

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