typename boost::math::tools::promote_args<T_y,T_loc,T_scale,T_shape>::type
lkj_cov_log(const Eigen::Matrix<T_y,Eigen::Dynamic,Eigen::Dynamic>& y,
const Eigen::Matrix<T_loc,Eigen::Dynamic,1>& mu,
const Eigen::Matrix<T_scale,Eigen::Dynamic,1>& sigma,
const T_shape& eta,
const Policy&) {
static const char* function = "stan::prob::lkj_cov_log(%1%)";
using stan::math::check_size_match;
using stan::math::check_finite;
using stan::math::check_positive;
using boost::math::tools::promote_args;
typename promote_args<T_y,T_loc,T_scale,T_shape>::type lp(0.0);
if (!check_size_match(function,
mu.rows(), "Rows of location parameter",
sigma.rows(), "columns of scale parameter",
&lp, Policy()))
return lp;
if (!check_size_match(function,
y.rows(), "Rows of random variable",
y.cols(), "columns of random variable",
&lp, Policy()))
return lp;
if (!check_size_match(function,
y.rows(), "Rows of random variable",
mu.rows(), "rows of location parameter",
&lp, Policy()))
return lp;
if (!check_positive(function, eta, "Shape parameter", &lp, Policy()))
return lp;
if (!check_finite(function, mu, "Location parameter", &lp, Policy()))
return lp;
if (!check_finite(function, sigma, "Scale parameter", &lp, Policy()))
return lp;
// FIXME: build vectorized versions
for (int m = 0; m < y.rows(); ++m)
for (int n = 0; n < y.cols(); ++n)
if (!check_finite(function, y(m,n), "Covariance matrix", &lp, Policy()))
return lp;
const unsigned int K = y.rows();
const Eigen::Array<T_y,Eigen::Dynamic,1> sds
= y.diagonal().array().sqrt();
for (unsigned int k = 0; k < K; k++) {
lp += lognormal_log<propto>(sds(k), mu(k), sigma(k), Policy());
}
if (stan::is_constant<typename stan::scalar_type<T_shape> >::value
&& eta == 1.0) {
// no need to rescale y into a correlation matrix
lp += lkj_corr_log<propto,T_y,T_shape,Policy>(y, eta, Policy());
return lp;
}
Eigen::DiagonalMatrix<T_y,Eigen::Dynamic> D(K);
D.diagonal() = sds.inverse();
lp += lkj_corr_log<propto,T_y,T_shape,Policy>(D * y * D, eta, Policy());
return lp;
}
typename boost::math::tools::promote_args<T_y,T_loc,T_scale,T_shape>::type
lkj_cov_log(const Eigen::Matrix<T_y,Eigen::Dynamic,Eigen::Dynamic>& y,
const T_loc& mu,
const T_scale& sigma,
const T_shape& eta,
const Policy&) {
static const char* function = "stan::prob::lkj_cov_log(%1%)";
using stan::math::check_finite;
using stan::math::check_positive;
using boost::math::tools::promote_args;
typename promote_args<T_y,T_loc,T_scale,T_shape>::type lp(0.0);
if (!check_positive(function, eta, "Shape parameter", &lp, Policy()))
return lp;
if (!check_finite(function, mu, "Location parameter", &lp, Policy()))
return lp;
if (!check_finite(function, sigma, "Scale parameter",
&lp, Policy()))
return lp;
const unsigned int K = y.rows();
const Eigen::Array<T_y,Eigen::Dynamic,1> sds
= y.diagonal().array().sqrt();
for (unsigned int k = 0; k < K; k++) {
lp += lognormal_log<propto>(sds(k), mu, sigma, Policy());
}
if (stan::is_constant<typename stan::scalar_type<T_shape> >::value
&& eta == 1.0) {
// no need to rescale y into a correlation matrix
lp += lkj_corr_log<propto>(y,eta,Policy());
return lp;
}
Eigen::DiagonalMatrix<T_y,Eigen::Dynamic> D(K);
D.diagonal() = sds.inverse();
lp += lkj_corr_log<propto,T_y,T_shape,Policy>(D * y * D, eta, Policy());
return lp;
}
