Automatic Differentiation
 
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multi_gp_cholesky_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_MULTI_GP_CHOLESKY_LOG_HPP
2#define STAN_MATH_PRIM_PROB_MULTI_GP_CHOLESKY_LOG_HPP
3
7
8namespace stan {
9namespace math {
33template <bool propto, typename T_y, typename T_covar, typename T_w>
35 const T_covar& L,
36 const T_w& w) {
37 return multi_gp_cholesky_lpdf<propto>(y, L, w);
38}
39
43template <typename T_y, typename T_covar, typename T_w>
45 const T_covar& L,
46 const T_w& w) {
47 return multi_gp_cholesky_lpdf<>(y, L, w);
48}
49
50} // namespace math
51} // namespace stan
52#endif
return_type_t< T_y, T_covar, T_w > multi_gp_cholesky_log(const T_y &y, const T_covar &L, const T_w &w)
The log of a multivariate Gaussian Process for the given y, w, and a Cholesky factor L of the kernel ...
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