Automatic Differentiation
 
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gamma_lpdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_GAMMA_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_GAMMA_LPDF_HPP
3
21#include <cmath>
22
23namespace stan {
24namespace math {
25
49template <bool propto, typename T_y, typename T_shape, typename T_inv_scale,
51 T_y, T_shape, T_inv_scale>* = nullptr>
53 const T_shape& alpha,
54 const T_inv_scale& beta) {
55 using T_partials_return = partials_return_t<T_y, T_shape, T_inv_scale>;
56 using T_y_ref = ref_type_if_not_constant_t<T_y>;
57 using T_alpha_ref = ref_type_if_not_constant_t<T_shape>;
59 static constexpr const char* function = "gamma_lpdf";
60 check_consistent_sizes(function, "Random variable", y, "Shape parameter",
61 alpha, "Inverse scale parameter", beta);
62 T_y_ref y_ref = y;
63 T_alpha_ref alpha_ref = alpha;
64 T_beta_ref beta_ref = beta;
65
66 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
67 decltype(auto) alpha_val = to_ref(as_value_column_array_or_scalar(alpha_ref));
68 decltype(auto) beta_val = to_ref(as_value_column_array_or_scalar(beta_ref));
69
70 check_positive_finite(function, "Random variable", y_val);
71 check_positive_finite(function, "Shape parameter", alpha_val);
72 check_positive_finite(function, "Inverse scale parameter", beta_val);
73
74 if (size_zero(y, alpha, beta)) {
75 return 0.0;
76 }
78 return 0.0;
79 }
80
81 auto ops_partials = make_partials_propagator(y_ref, alpha_ref, beta_ref);
82
83 scalar_seq_view<decltype(y_val)> y_vec(y_val);
84 for (size_t n = 0; n < stan::math::size(y); n++) {
85 if (y_vec[n] < 0) {
86 return LOG_ZERO;
87 }
88 }
89
90 size_t N = max_size(y, alpha, beta);
91 T_partials_return logp(0.0);
93 logp = -sum(lgamma(alpha_val)) * N / math::size(alpha);
94 }
95 const auto& log_y = to_ref_if<is_constant_all<T_shape>::value>(log(y_val));
97 const auto& log_beta
98 = to_ref_if<!is_constant_all<T_shape>::value>(log(beta_val));
99 logp += sum(alpha_val * log_beta) * N / max_size(alpha, beta);
101 partials<1>(ops_partials) = log_beta + log_y - digamma(alpha_val);
102 }
103 }
105 logp += sum((alpha_val - 1.0) * log_y) * N / max_size(alpha, y);
106 }
108 logp -= sum(beta_val * y_val) * N / max_size(beta, y);
109 }
110
112 partials<0>(ops_partials) = (alpha_val - 1) / y_val - beta_val;
113 }
115 partials<2>(ops_partials) = alpha_val / beta_val - y_val;
116 }
117 return ops_partials.build(logp);
118}
119
120template <typename T_y, typename T_shape, typename T_inv_scale>
122 const T_y& y, const T_shape& alpha, const T_inv_scale& beta) {
123 return gamma_lpdf<false>(y, alpha, beta);
124}
125
126} // namespace math
127} // namespace stan
128#endif
scalar_seq_view provides a uniform sequence-like wrapper around either a scalar or a sequence of scal...
require_all_not_t< is_nonscalar_prim_or_rev_kernel_expression< std::decay_t< Types > >... > require_all_not_nonscalar_prim_or_rev_kernel_expression_t
Require none of the types satisfy is_nonscalar_prim_or_rev_kernel_expression.
return_type_t< T_y_cl, T_shape_cl, T_inv_scale_cl > gamma_lpdf(const T_y_cl &y, const T_shape_cl &alpha, const T_inv_scale_cl &beta)
The log of a gamma density for y with the specified shape and inverse scale parameters.
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
int64_t size(const T &m)
Returns the size (number of the elements) of a matrix_cl or var_value<matrix_cl<T>>.
Definition size.hpp:19
static constexpr double LOG_ZERO
The natural logarithm of 0, .
Definition constants.hpp:68
bool size_zero(const T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
Definition size_zero.hpp:19
fvar< T > log(const fvar< T > &x)
Definition log.hpp:18
auto as_value_column_array_or_scalar(T &&a)
Extract the value from an object and for eigen vectors and std::vectors convert to an eigen column ar...
void check_consistent_sizes(const char *)
Trivial no input case, this function is a no-op.
ref_type_t< T && > to_ref(T &&a)
This evaluates expensive Eigen expressions.
Definition to_ref.hpp:17
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
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_size.hpp:20
fvar< T > beta(const fvar< T > &x1, const fvar< T > &x2)
Return fvar with the beta function applied to the specified arguments and its gradient.
Definition beta.hpp:51
auto make_partials_propagator(Ops &&... ops)
Construct an partials_propagator.
void check_positive_finite(const char *function, const char *name, const T_y &y)
Check if y is positive and finite.
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.
Definition digamma.hpp:23
typename ref_type_if<!is_constant< T >::value, T >::type ref_type_if_not_constant_t
Definition ref_type.hpp:62
typename partials_return_type< Args... >::type partials_return_t
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Extends std::true_type when instantiated with zero or more template parameters, all of which extend t...
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...