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laplace_latent_neg_binomial_2_log_rng.hpp
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1#ifndef STAN_MATH_MIX_PROB_LAPLACE_LATENT_NEG_BINOMIAL_2_LOG_RNG_HPP
2#define STAN_MATH_MIX_PROB_LAPLACE_LATENT_NEG_BINOMIAL_2_LOG_RNG_HPP
3
7
8namespace stan {
9namespace math {
10
38template <typename Eta, typename ThetaVec, typename Mean, typename CovarFun,
39 typename CovarArgs, typename RNG,
40 require_eigen_vector_t<ThetaVec>* = nullptr>
42 const std::vector<int>& y, const std::vector<int>& y_index, Eta&& eta,
43 Mean&& mean, CovarFun&& covariance_function, CovarArgs&& covar_args,
44 ThetaVec&& theta_0, const double tolerance, const int max_num_steps,
45 const int hessian_block_size, const int solver,
46 const int max_steps_line_search, RNG& rng, std::ostream* msgs) {
47 laplace_options_user_supplied ops{hessian_block_size, solver,
48 max_steps_line_search, tolerance,
49 max_num_steps, value_of(theta_0)};
50 return laplace_base_rng(
52 std::forward_as_tuple(std::forward<Eta>(eta), y, y_index,
53 std::forward<Mean>(mean)),
54 std::forward<CovarFun>(covariance_function),
55 std::forward<CovarArgs>(covar_args), ops, rng, msgs);
56}
57
82template <typename Eta, typename Mean, typename CovarFun, typename CovarArgs,
83 typename RNG>
85 const std::vector<int>& y, const std::vector<int>& y_index, Eta&& eta,
86 Mean&& mean, CovarFun&& covariance_function, CovarArgs&& covar_args,
87 RNG& rng, std::ostream* msgs) {
88 return laplace_base_rng(
90 std::forward_as_tuple(std::forward<Eta>(eta), y, y_index,
91 std::forward<Mean>(mean)),
92 std::forward<CovarFun>(covariance_function),
93 std::forward<CovarArgs>(covar_args), laplace_options_default{}, rng,
94 msgs);
95}
96
97} // namespace math
98} // namespace stan
99
100#endif
scalar_type_t< T > mean(const T &m)
Returns the sample mean (i.e., average) of the coefficients in the specified std vector,...
Definition mean.hpp:20
Eigen::VectorXd laplace_base_rng(LLFunc &&ll_fun, LLArgs &&ll_args, CovarFun &&covariance_function, CovarArgs &&covar_args, const laplace_options< InitTheta > &options, RNG &rng, std::ostream *msgs)
In a latent gaussian model,.
T value_of(const fvar< T > &v)
Return the value of the specified variable.
Definition value_of.hpp:18
Eigen::VectorXd laplace_latent_tol_neg_binomial_2_log_rng(const std::vector< int > &y, const std::vector< int > &y_index, Eta &&eta, Mean &&mean, CovarFun &&covariance_function, CovarArgs &&covar_args, ThetaVec &&theta_0, const double tolerance, const int max_num_steps, const int hessian_block_size, const int solver, const int max_steps_line_search, RNG &rng, std::ostream *msgs)
In a latent gaussian model,.
Eigen::VectorXd laplace_latent_neg_binomial_2_log_rng(const std::vector< int > &y, const std::vector< int > &y_index, Eta &&eta, Mean &&mean, CovarFun &&covariance_function, CovarArgs &&covar_args, RNG &rng, std::ostream *msgs)
In a latent gaussian model,.
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...