laplace_marginal_poisson_log_lpmf.hpp

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#ifndef STAN_MATH_MIX_PROB_LAPLACE_MARGINAL_POISSON_LOG_LPMF_HPP
#define STAN_MATH_MIX_PROB_LAPLACE_MARGINAL_POISSON_LOG_LPMF_HPP
 
#include <stan/math/mix/functor/laplace_likelihood.hpp>
#include <stan/math/mix/functor/laplace_marginal_density.hpp>
#include <stan/math/mix/meta.hpp>
#include <stan/math/rev/fun/dot_product.hpp>
#include <stan/math/rev/fun/exp.hpp>
#include <stan/math/fwd/fun/exp.hpp>
#include <stan/math/rev/fun/sum.hpp>
#include <stan/math/fwd/fun/sum.hpp>
#include <stan/math/fwd/fun/lgamma.hpp>
#include <stan/math/rev/fun/lgamma.hpp>
#include <stan/math/prim/fun/add.hpp>
 
namespace stan {
namespace math {
 
struct poisson_log_likelihood {
  template <typename Theta, typename YVec, typename Mean,
            require_eigen_vector_t<Theta>* = nullptr>
  inline auto operator()(const Theta& theta, const YVec& y,
                         const std::vector<int>& y_index, Mean&& mean,
                         std::ostream* /*pstream*/) const {
    Eigen::VectorXd counts_per_group = Eigen::VectorXd::Zero(theta.size());
    Eigen::VectorXd n_per_group = Eigen::VectorXd::Zero(theta.size());
 
    double norm_constant = 0;
    for (size_t i = 0; i < y_index.size(); i++) {
      counts_per_group(y_index[i] - 1) += y[i];
      n_per_group(y_index[i] - 1) += 1;
      norm_constant -= lgamma(y[i] + 1.0);
    }
 
    auto theta_offset = to_ref(add(theta, mean));
 
    return norm_constant + dot_product(theta_offset, counts_per_group)
           - dot_product(n_per_group, exp(theta_offset));
  }
};
 
template <bool propto = false, typename Mean, typename CovarFun,
          typename CovarArgs, typename OpsTuple>
inline auto laplace_marginal_tol_poisson_log_lpmf(
    const std::vector<int>& y, const std::vector<int>& y_index, Mean&& mean,
    int hessian_block_size, CovarFun&& covariance_function,
    CovarArgs&& covar_args, OpsTuple&& ops, std::ostream* msgs) {
  auto options
      = internal::tuple_to_laplace_options(std::forward<OpsTuple>(ops));
  options.hessian_block_size = hessian_block_size;
  return laplace_marginal_density(
      poisson_log_likelihood{},
      std::forward_as_tuple(y, y_index, std::forward<Mean>(mean)),
      covariance_function, std::forward<CovarArgs>(covar_args),
      std::move(options), msgs);
}
 
template <bool propto = false, typename CovarFun, typename CovarArgs,
          typename Mean>
inline auto laplace_marginal_poisson_log_lpmf(
    const std::vector<int>& y, const std::vector<int>& y_index, Mean&& mean,
    int hessian_block_size, CovarFun&& covariance_function,
    CovarArgs&& covar_args, std::ostream* msgs) {
  auto options = laplace_options_default{hessian_block_size};
  return laplace_marginal_density(
      poisson_log_likelihood{},
      std::forward_as_tuple(y, y_index, std::forward<Mean>(mean)),
      covariance_function, std::forward<CovarArgs>(covar_args), options, msgs);
}
 
}  // namespace math
}  // namespace stan
 
#endif