Automatic Differentiation
 
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laplace_marginal_poisson_log_lpmf.hpp
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1#ifndef STAN_MATH_MIX_PROB_LAPLACE_MARGINAL_POISSON_LOG_LPMF_HPP
2#define STAN_MATH_MIX_PROB_LAPLACE_MARGINAL_POISSON_LOG_LPMF_HPP
3
12
13namespace stan {
14namespace math {
15
31 template <typename Theta, typename YVec, typename Mean,
33 inline auto operator()(const Theta& theta, const YVec& y,
34 const std::vector<int>& y_index, Mean&& mean,
35 std::ostream* /*pstream*/) const {
36 Eigen::VectorXd counts_per_group = Eigen::VectorXd::Zero(theta.size());
37 Eigen::VectorXd n_per_group = Eigen::VectorXd::Zero(theta.size());
38
39 for (int i = 0; i < theta.size(); i++) {
40 counts_per_group(y_index[i] - 1) += y[i];
41 n_per_group(y_index[i] - 1) += 1;
42 }
43
44 auto theta_offset = to_ref(add(theta, mean));
45
46 return -sum(lgamma(add(counts_per_group, 1)))
47 + dot_product(theta_offset, counts_per_group)
48 - dot_product(n_per_group, exp(theta_offset));
49 }
50};
51
71template <bool propto = false, typename Mean, typename CovarFun,
72 typename CovarArgs, typename OpsTuple>
74 const std::vector<int>& y, const std::vector<int>& y_index, Mean&& mean,
75 CovarFun&& covariance_function, CovarArgs&& covar_args, OpsTuple&& ops,
76 std::ostream* msgs) {
79 std::forward_as_tuple(y, y_index, std::forward<Mean>(mean)),
80 covariance_function, std::forward<CovarArgs>(covar_args),
81 internal::tuple_to_laplace_options(std::forward<OpsTuple>(ops)), msgs);
82}
83
100template <bool propto = false, typename CovarFun, typename CovarArgs,
101 typename Mean>
102inline auto laplace_marginal_poisson_log_lpmf(const std::vector<int>& y,
103 const std::vector<int>& y_index,
104 Mean&& mean,
105 CovarFun&& covariance_function,
106 CovarArgs&& covar_args,
107 std::ostream* msgs) {
110 std::forward_as_tuple(y, y_index, std::forward<Mean>(mean)),
111 covariance_function, std::forward<CovarArgs>(covar_args),
113}
114
115} // namespace math
116} // namespace stan
117
118#endif
require_t< is_eigen_vector< std::decay_t< T > > > require_eigen_vector_t
Require type satisfies is_eigen_vector.
addition_< as_operation_cl_t< T_a >, as_operation_cl_t< T_b > > add(T_a &&a, T_b &&b)
Reference for calculations of marginal and its gradients: Margossian et al (2020),...
constexpr auto tuple_to_laplace_options(Options &&ops)
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
auto laplace_marginal_tol_poisson_log_lpmf(const std::vector< int > &y, const std::vector< int > &y_index, Mean &&mean, CovarFun &&covariance_function, CovarArgs &&covar_args, OpsTuple &&ops, std::ostream *msgs)
Wrapper function around the laplace_marginal function for a log poisson likelihood.
auto laplace_marginal_density(LLFun &&ll_fun, LLTupleArgs &&ll_args, CovarFun &&covariance_function, CovarArgs &&covar_args, const laplace_options< InitTheta > &options, std::ostream *msgs)
For a latent Gaussian model with global parameters phi, latent variables theta, and observations y,...
auto laplace_marginal_poisson_log_lpmf(const std::vector< int > &y, const std::vector< int > &y_index, Mean &&mean, CovarFun &&covariance_function, CovarArgs &&covar_args, std::ostream *msgs)
Wrapper function around the laplace_marginal function for a log poisson likelihood.
fvar< T > lgamma(const fvar< T > &x)
Return the natural logarithm of the gamma function applied to the specified argument.
Definition lgamma.hpp:21
auto sum(const std::vector< T > &m)
Return the sum of the entries of the specified standard vector.
Definition sum.hpp:23
ref_type_t< T && > to_ref(T &&a)
This evaluates expensive Eigen expressions.
Definition to_ref.hpp:18
auto dot_product(const T_a &a, const T_b &b)
Returns the dot product of the specified vectors.
fvar< T > exp(const fvar< T > &x)
Definition exp.hpp:15
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
auto operator()(const Theta &theta, const YVec &y, const std::vector< int > &y_index, Mean &&mean, std::ostream *) const
Returns the lpmf for a Poisson with a log link across multiple groups.