Stan Math Library  2.20.0
reverse mode automatic differentiation
wiener_log.hpp
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1 #ifndef STAN_MATH_PRIM_MAT_PROB_WIENER_LOG_HPP
2 #define STAN_MATH_PRIM_MAT_PROB_WIENER_LOG_HPP
3 
6 #include <boost/math/tools/promotion.hpp>
7 
8 namespace stan {
9 namespace math {
10 
31 template <bool propto, typename T_y, typename T_alpha, typename T_tau,
32  typename T_beta, typename T_delta>
34  const T_y& y, const T_alpha& alpha, const T_tau& tau, const T_beta& beta,
35  const T_delta& delta) {
36  return wiener_lpdf<propto, T_y, T_alpha, T_tau, T_beta, T_delta>(
37  y, alpha, tau, beta, delta);
38 }
39 
43 template <typename T_y, typename T_alpha, typename T_tau, typename T_beta,
44  typename T_delta>
46 wiener_log(const T_y& y, const T_alpha& alpha, const T_tau& tau,
47  const T_beta& beta, const T_delta& delta) {
48  return wiener_lpdf<T_y, T_alpha, T_tau, T_beta, T_delta>(y, alpha, tau, beta,
49  delta);
50 }
51 
52 } // namespace math
53 } // namespace stan
54 #endif
return_type< T_y, T_alpha, T_tau, T_beta, T_delta >::type wiener_log(const T_y &y, const T_alpha &alpha, const T_tau &tau, const T_beta &beta, const T_delta &delta)
The log of the first passage time density function for a (Wiener) drift diffusion model for the given...
Definition: wiener_log.hpp:33
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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