Stan  2.10.0
probability, sampling & optimization
adapt_unit_e_static_hmc.hpp
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1 #ifndef STAN_MCMC_HMC_STATIC_ADAPT_UNIT_E_STATIC_HMC_HPP
2 #define STAN_MCMC_HMC_STATIC_ADAPT_UNIT_E_STATIC_HMC_HPP
3 
7 
8 namespace stan {
9  namespace mcmc {
16  template <class Model, class BaseRNG>
17  class adapt_unit_e_static_hmc : public unit_e_static_hmc<Model, BaseRNG>,
18  public stepsize_adapter {
19  public:
20  adapt_unit_e_static_hmc(const Model& model, BaseRNG& rng)
21  : unit_e_static_hmc<Model, BaseRNG>(model, rng) { }
22 
24 
25  sample
26  transition(sample& init_sample,
29  sample s
31  info_writer,
32  error_writer);
33 
34  if (this->adapt_flag_) {
36  s.accept_stat());
37  this->update_L_();
38  }
39 
40  return s;
41  }
42 
46  }
47  };
48 
49  } // mcmc
50 } // stan
51 #endif
void complete_adaptation(double &epsilon)
adapt_unit_e_static_hmc(const Model &model, BaseRNG &rng)
double accept_stat() const
Definition: sample.hpp:41
Probability, optimization and sampling library.
void learn_stepsize(double &epsilon, double adapt_stat)
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration t...
base_writer is an abstract base class defining the interface for Stan writer callbacks.
Definition: base_writer.hpp:20
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
virtual void disengage_adaptation()
Hamiltonian Monte Carlo implementation using the endpoint of trajectories with a static integration t...
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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