1 #ifndef STAN_MCMC_HMC_XHMC_ADAPT_DENSE_E_XHMC_HPP
2 #define STAN_MCMC_HMC_XHMC_ADAPT_DENSE_E_XHMC_HPP
15 template <
class Model,
class BaseRNG>
void complete_adaptation(double &epsilon)
dense_e_metric< Model, BaseRNG >::PointType z_
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
double accept_stat() const
Probability, optimization and sampling library.
void learn_stepsize(double &epsilon, double adapt_stat)
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disinteg...
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disinteg...
bool learn_covariance(Eigen::MatrixXd &covar, const Eigen::VectorXd &q)
stepsize_adaptation stepsize_adaptation_
covar_adaptation covar_adaptation_
base_writer is an abstract base class defining the interface for Stan writer callbacks.
virtual void disengage_adaptation()
adapt_dense_e_xhmc(const Model &model, BaseRNG &rng)
void disengage_adaptation()
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
void init_stepsize(interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)