1 #ifndef STAN_MCMC_HMC_NUTS_ADAPT_DENSE_E_NUTS_HPP
2 #define STAN_MCMC_HMC_NUTS_ADAPT_DENSE_E_NUTS_HPP
15 template <
class Model,
class BaseRNG>
void complete_adaptation(double &epsilon)
dense_e_metric< Model, BaseRNG >::PointType z_
double accept_stat() const
Probability, optimization and sampling library.
void learn_stepsize(double &epsilon, double adapt_stat)
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
bool learn_covariance(Eigen::MatrixXd &covar, const Eigen::VectorXd &q)
void disengage_adaptation()
stepsize_adaptation stepsize_adaptation_
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
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_nuts(const Model &model, BaseRNG &rng)
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and d...
The No-U-Turn sampler (NUTS) with multinomial sampling with a Gaussian-Euclidean disintegration and a...
void init_stepsize(interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)