Automatic Differentiation
 
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normal_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_NORMAL_LOG_HPP
2#define STAN_MATH_PRIM_PROB_NORMAL_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
31template <bool propto, typename T_y, typename T_loc, typename T_scale>
33 const T_loc& mu,
34 const T_scale& sigma) {
35 return normal_lpdf<propto, T_y, T_loc, T_scale>(y, mu, sigma);
36}
37
41template <typename T_y, typename T_loc, typename T_scale>
43 const T_loc& mu,
44 const T_scale& sigma) {
45 return normal_lpdf<T_y, T_loc, T_scale>(y, mu, sigma);
46}
47
48} // namespace math
49} // namespace stan
50#endif
return_type_t< T_y, T_loc, T_scale > normal_log(const T_y &y, const T_loc &mu, const T_scale &sigma)
The log of the normal density for the specified scalar(s) given the specified mean(s) and deviation(s...
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9