Automatic Differentiation
 
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uniform_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_UNIFORM_LOG_HPP
2#define STAN_MATH_PRIM_PROB_UNIFORM_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
34template <bool propto, typename T_y, typename T_low, typename T_high>
35return_type_t<T_y, T_low, T_high> uniform_log(const T_y& y, const T_low& alpha,
36 const T_high& beta) {
37 return uniform_lpdf<propto, T_y, T_low, T_high>(y, alpha, beta);
38}
39
43template <typename T_y, typename T_low, typename T_high>
45 const T_low& alpha,
46 const T_high& beta) {
47 return uniform_lpdf<T_y, T_low, T_high>(y, alpha, beta);
48}
49
50} // namespace math
51} // namespace stan
52#endif
return_type_t< T_y, T_low, T_high > uniform_log(const T_y &y, const T_low &alpha, const T_high &beta)
The log of a uniform density for the given y, lower, and upper bound.
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