Stan  2.10.0
probability, sampling & optimization
adapt_softabs_static_uniform.hpp
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1 #ifndef STAN_MCMC_HMC_STATIC_ADAPT_SOFTABS_STATIC_HMC_HPP
2 #define STAN_MCMC_HMC_STATIC_ADAPT_SOFTABS_STATIC_HMC_HPP
3 
7 
8 namespace stan {
9  namespace mcmc {
16  template <class Model, class BaseRNG>
18  public softabs_static_uniform<Model, BaseRNG>,
19  public stepsize_adapter {
20  public:
21  adapt_softabs_static_uniform(const Model& model, BaseRNG& rng):
22  softabs_static_uniform<Model, BaseRNG>(model, rng) { }
23 
25 
27  sample& init_sample,
30  sample s
32  info_writer,
33  error_writer);
34 
35  if (this->adapt_flag_) {
37  s.accept_stat());
38  this->update_L_();
39  }
40 
41  return s;
42  }
43 
47  }
48  };
49  } // mcmc
50 } // stan
51 #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)
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 that uniformly samples from trajectories with a static integra...
base_writer is an abstract base class defining the interface for Stan writer callbacks.
Definition: base_writer.hpp:20
virtual void disengage_adaptation()
Hamiltonian Monte Carlo implementation that uniformly samples from trajectories with a static integra...
stepsize_adaptation stepsize_adaptation_
sample transition(sample &init_sample, interface_callbacks::writer::base_writer &info_writer, interface_callbacks::writer::base_writer &error_writer)
adapt_softabs_static_uniform(const Model &model, BaseRNG &rng)

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