Stan Math Library  2.15.0
reverse mode automatic differentiation
read_cov_L.hpp
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1 #ifndef STAN_MATH_PRIM_MAT_FUN_READ_COV_L_HPP
2 #define STAN_MATH_PRIM_MAT_FUN_READ_COV_L_HPP
3 
6 
7 namespace stan {
8  namespace math {
9 
20  template <typename T>
21  Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
22  read_cov_L(const Eigen::Array<T, Eigen::Dynamic, 1>& CPCs,
23  const Eigen::Array<T, Eigen::Dynamic, 1>& sds,
24  T& log_prob) {
25  size_t K = sds.rows();
26  // adjust due to transformation from correlations to covariances
27  log_prob += (sds.log().sum() + LOG_2) * K;
28  return sds.matrix().asDiagonal() * read_corr_L(CPCs, K, log_prob);
29  }
30 
31  }
32 }
33 #endif
const double LOG_2
The natural logarithm of 2, .
Definition: constants.hpp:32
Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > read_cov_L(const Eigen::Array< T, Eigen::Dynamic, 1 > &CPCs, const Eigen::Array< T, Eigen::Dynamic, 1 > &sds, T &log_prob)
This is the function that should be called prior to evaluating the density of any elliptical distribu...
Definition: read_cov_L.hpp:22
Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > read_corr_L(const Eigen::Array< T, Eigen::Dynamic, 1 > &CPCs, size_t K)
Return the Cholesky factor of the correlation matrix of the specified dimensionality corresponding to...
Definition: read_corr_L.hpp:36

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