Automatic Differentiation
 
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◆ gp_exp_quad_cov() [6/7]

template<typename T_x1 , typename T_x2 , typename T_s , typename T_l >
Eigen::Matrix< return_type_t< T_x1, T_x2, T_s, T_l >, Eigen::Dynamic, Eigen::Dynamic > stan::math::gp_exp_quad_cov ( const std::vector< Eigen::Matrix< T_x1, -1, 1 > > &  x1,
const std::vector< Eigen::Matrix< T_x2, -1, 1 > > &  x2,
const T_s &  sigma,
const std::vector< T_l > &  length_scale 
)
inline

Returns a squared exponential kernel.

This function is for the cross covariance matrix needed to compute the posterior predictive density.

Template Parameters
T_x1type of first std::vector of elements
T_x2type of second std::vector of elements
T_stype of sigma
T_ltype of length scale
Parameters
x1std::vector of Eigen vectors of scalars.
x2std::vector of Eigen vectors of scalars.
sigmastandard deviation
length_scalestd::vector of length scale
Returns
squared distance
Exceptions
std::domain_errorif sigma <= 0, l <= 0, or x is nan or infinite

Definition at line 258 of file gp_exp_quad_cov.hpp.