Stan  2.10.0
probability, sampling & optimization
adapt_unit_e_xhmc.hpp
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1 #ifndef STAN_MCMC_HMC_XHMC_ADAPT_UNIT_E_XHMC_HPP
2 #define STAN_MCMC_HMC_XHMC_ADAPT_UNIT_E_XHMC_HPP
3 
7 
8 namespace stan {
9  namespace mcmc {
15  template <class Model, class BaseRNG>
16  class adapt_unit_e_xhmc: public unit_e_xhmc<Model, BaseRNG>,
17  public stepsize_adapter {
18  public:
19  adapt_unit_e_xhmc(const Model& model, BaseRNG& rng)
20  : unit_e_xhmc<Model, BaseRNG>(model, rng) {}
21 
23 
24  sample
25  transition(sample& init_sample,
28  sample s
30  info_writer,
31  error_writer);
32 
33  if (this->adapt_flag_)
35  s.accept_stat());
36 
37  return s;
38  }
39 
43  }
44  };
45 
46  } // mcmc
47 } // stan
48 #endif
void complete_adaptation(double &epsilon)
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
Definition: base_xhmc.hpp:88
double accept_stat() const
Definition: sample.hpp:41
adapt_unit_e_xhmc(const Model &model, BaseRNG &rng)
Probability, optimization and sampling library.
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disinteg...
void learn_stepsize(double &epsilon, double adapt_stat)
base_writer is an abstract base class defining the interface for Stan writer callbacks.
Definition: base_writer.hpp:20
virtual void disengage_adaptation()
Exhausive Hamiltonian Monte Carlo (XHMC) with multinomial sampling with a Gaussian-Euclidean disinteg...
Definition: unit_e_xhmc.hpp:16
stepsize_adaptation stepsize_adaptation_
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)

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