Automatic Differentiation
 
Loading...
Searching...
No Matches

◆ multi_student_t_cholesky_lpdf() [1/2]

template<bool propto, typename T_y , typename T_dof , typename T_loc , typename T_covar , require_any_not_vector_vt< is_stan_scalar, T_y, T_dof, T_loc > * = nullptr, require_all_not_nonscalar_prim_or_rev_kernel_expression_t< T_y, T_loc, T_covar > * = nullptr>
return_type_t< T_y, T_dof, T_loc, T_covar > stan::math::multi_student_t_cholesky_lpdf ( const T_y &  y,
const T_dof &  nu,
const T_loc &  mu,
const T_covar &  L 
)

The log of the multivariate student t density for the given y, mu, nu, and a Cholesky factor L of the scale matrix.

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

This version of the function is vectorized on y and mu.

Parameters
yscalar vector of random variates
nuscalar degrees of freedom
mulocation vector
Lcholesky decomposition of the scale matrix of the multivariate student t distribution.
Returns
The log of the multivariate student t density.
Exceptions
std::domain_errorif LL' is not square, not symmetric, or not semi-positive definite.
Template Parameters
T_yType of scalar.
T_dofType of scalar.
T_locType of location.
T_covarType of scale.
Returns
log probability of the multivariate student t distribution.

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

This version of the function is vectorized on y and mu.

Parameters
yA scalar vector.
nuA scalar degrees of freedom.
muA location vector.
LThe Cholesky decomposition of the scale matrix of the multivariate student t distribution.
Returns
The log of the multivariate student t density.
Exceptions
std::domain_errorif LL' is not square, not symmetric, or not semi-positive definite.
Template Parameters
T_yType of scalar.
T_dofType of scalar.
T_locType of location.
T_covarType of scale.
Returns
log probability of the multivariate student t distribution.

Definition at line 57 of file multi_student_t_cholesky_lpdf.hpp.