Automatic Differentiation
 
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◆ multi_normal_cholesky_lpdf() [1/3]

template<bool propto, typename T_y_cl , typename T_loc_cl , typename T_covar_cl , require_all_prim_or_rev_kernel_expression_t< T_y_cl, T_loc_cl, T_covar_cl > * = nullptr>
return_type_t< T_y_cl, T_loc_cl, T_covar_cl > stan::math::multi_normal_cholesky_lpdf ( const T_y_cl &  y,
const T_loc_cl &  mu,
const T_covar_cl &  L 
)
inline

The log of the multivariate normal density for the given y, mu, and a Cholesky factor L of the variance matrix.

Sigma = LL', a square, semi-positive definite matrix.

Analytic expressions taken from http://qwone.com/~jason/writing/multivariateNormal.pdf written by Jason D. M. Rennie.

Parameters
yA scalar vector
muThe mean vector of the multivariate normal distribution.
LThe Cholesky decomposition of a variance matrix of the multivariate normal distribution
Returns
The log of the multivariate normal density.
Exceptions
std::domain_errorif LL' is not square, not symmetric, or not semi-positive definite.
Template Parameters
T_yType of scalar.
T_locType of location.
T_covarType of scale.

Definition at line 42 of file multi_normal_cholesky_lpdf.hpp.