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template<bool jacobian_adjust_transform, class M > |
double | stan::model::log_prob_propto (const M &model, std::vector< double > ¶ms_r, std::vector< int > ¶ms_i, std::ostream *msgs=0) |
| Helper function to calculate log probability for double scalars up to a proportion. More...
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template<bool propto, bool jacobian_adjust_transform, class M > |
double | stan::model::log_prob_grad (const M &model, std::vector< double > ¶ms_r, std::vector< int > ¶ms_i, std::vector< double > &gradient, std::ostream *msgs=0) |
| Compute the gradient using reverse-mode automatic differentiation, writing the result into the specified gradient, using the specified perturbation. More...
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template<bool jacobian_adjust_transform, class M > |
double | stan::model::log_prob_propto (const M &model, Eigen::VectorXd ¶ms_r, std::ostream *msgs=0) |
| Helper function to calculate log probability for double scalars up to a proportion. More...
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template<bool propto, bool jacobian_adjust_transform, class M > |
double | stan::model::log_prob_grad (const M &model, Eigen::VectorXd ¶ms_r, Eigen::VectorXd &gradient, std::ostream *msgs=0) |
| Compute the gradient using reverse-mode automatic differentiation, writing the result into the specified gradient, using the specified perturbation. More...
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template<bool propto, bool jacobian_adjust_transform, class M > |
void | stan::model::finite_diff_grad (const M &model, std::vector< double > ¶ms_r, std::vector< int > ¶ms_i, std::vector< double > &grad, double epsilon=1e-6, std::ostream *msgs=0) |
| Compute the gradient using finite differences for the specified parameters, writing the result into the specified gradient, using the specified perturbation. More...
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template<bool propto, bool jacobian_adjust_transform, class M > |
int | stan::model::test_gradients (const M &model, std::vector< double > ¶ms_r, std::vector< int > ¶ms_i, double epsilon, double error, stan::interface_callbacks::writer::base_writer &writer) |
| Test the log_prob_grad() function's ability to produce accurate gradients using finite differences. More...
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template<bool propto, bool jacobian_adjust_transform, class M > |
double | stan::model::grad_hess_log_prob (const M &model, std::vector< double > ¶ms_r, std::vector< int > ¶ms_i, std::vector< double > &gradient, std::vector< double > &hessian, std::ostream *msgs=0) |
| Evaluate the log-probability, its gradient, and its Hessian at params_r. More...
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template<class M > |
void | stan::model::gradient (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_f, std::ostream *msgs=0) |
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template<class M > |
void | stan::model::gradient (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_f, stan::interface_callbacks::writer::base_writer &writer) |
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template<class M > |
void | stan::model::hessian (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_f, Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &hess_f, std::ostream *msgs=0) |
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template<class M > |
void | stan::model::gradient_dot_vector (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &v, double &f, double &grad_f_dot_v, std::ostream *msgs=0) |
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template<class M > |
void | stan::model::hessian_times_vector (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &v, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &hess_f_dot_v, std::ostream *msgs=0) |
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template<class M > |
void | stan::model::grad_tr_mat_times_hessian (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &X, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_tr_X_hess_f, std::ostream *msgs=0) |
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