Automatic Differentiation
 
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gamma_cdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_GAMMA_CDF_HPP
2#define STAN_MATH_PRIM_PROB_GAMMA_CDF_HPP
3
16#include <cmath>
17
18namespace stan {
19namespace math {
20
35template <typename T_y, typename T_shape, typename T_inv_scale>
37 const T_y& y, const T_shape& alpha, const T_inv_scale& beta) {
38 using T_partials_return = partials_return_t<T_y, T_shape, T_inv_scale>;
39 using T_y_ref = ref_type_t<T_y>;
40 using T_alpha_ref = ref_type_t<T_shape>;
41 using T_beta_ref = ref_type_t<T_inv_scale>;
42 using std::exp;
43 static constexpr const char* function = "gamma_cdf";
44 check_consistent_sizes(function, "Random variable", y, "Shape parameter",
45 alpha, "Inverse scale parameter", beta);
46 T_y_ref y_ref = y;
47 T_alpha_ref alpha_ref = alpha;
48 T_beta_ref beta_ref = beta;
49 check_positive_finite(function, "Shape parameter", alpha_ref);
50 check_positive_finite(function, "Inverse scale parameter", beta_ref);
51 check_nonnegative(function, "Random variable", y_ref);
52
53 if (size_zero(y, alpha, beta)) {
54 return 1.0;
55 }
56
57 T_partials_return P(1.0);
58 auto ops_partials = make_partials_propagator(y_ref, alpha_ref, beta_ref);
59
60 scalar_seq_view<T_y_ref> y_vec(y_ref);
61 scalar_seq_view<T_alpha_ref> alpha_vec(alpha_ref);
62 scalar_seq_view<T_beta_ref> beta_vec(beta_ref);
63 size_t N = max_size(y, alpha, beta);
64
65 // Explicit return for extreme values
66 // The gradients are technically ill-defined, but treated as zero
67 for (size_t i = 0; i < stan::math::size(y); i++) {
68 if (y_vec.val(i) == 0) {
69 return ops_partials.build(0.0);
70 }
71 }
72
73 using std::exp;
74 using std::pow;
75
76 VectorBuilder<is_autodiff_v<T_shape>, T_partials_return, T_shape> gamma_vec(
77 math::size(alpha));
78 VectorBuilder<is_autodiff_v<T_shape>, T_partials_return, T_shape> digamma_vec(
79 math::size(alpha));
80
81 if constexpr (is_autodiff_v<T_shape>) {
82 for (size_t i = 0; i < stan::math::size(alpha); i++) {
83 const T_partials_return alpha_dbl = alpha_vec.val(i);
84 gamma_vec[i] = tgamma(alpha_dbl);
85 digamma_vec[i] = digamma(alpha_dbl);
86 }
87 }
88
89 for (size_t n = 0; n < N; n++) {
90 // Explicit results for extreme values
91 // The gradients are technically ill-defined, but treated as zero
92 if (y_vec.val(n) == INFTY) {
93 continue;
94 }
95
96 const T_partials_return y_dbl = y_vec.val(n);
97 const T_partials_return alpha_dbl = alpha_vec.val(n);
98 const T_partials_return beta_dbl = beta_vec.val(n);
99
100 const T_partials_return Pn = gamma_p(alpha_dbl, beta_dbl * y_dbl);
101
102 P *= Pn;
103
104 if constexpr (is_autodiff_v<T_y>) {
105 partials<0>(ops_partials)[n] += beta_dbl * exp(-beta_dbl * y_dbl)
106 * pow(beta_dbl * y_dbl, alpha_dbl - 1)
107 / tgamma(alpha_dbl) / Pn;
108 }
109 if constexpr (is_autodiff_v<T_shape>) {
110 partials<1>(ops_partials)[n]
111 -= grad_reg_inc_gamma(alpha_dbl, beta_dbl * y_dbl, gamma_vec[n],
112 digamma_vec[n])
113 / Pn;
114 }
115 if constexpr (is_autodiff_v<T_inv_scale>) {
116 partials<2>(ops_partials)[n] += y_dbl * exp(-beta_dbl * y_dbl)
117 * pow(beta_dbl * y_dbl, alpha_dbl - 1)
118 / tgamma(alpha_dbl) / Pn;
119 }
120 }
121
122 if constexpr (is_autodiff_v<T_y>) {
123 for (size_t n = 0; n < stan::math::size(y); ++n) {
124 partials<0>(ops_partials)[n] *= P;
125 }
126 }
127 if constexpr (is_autodiff_v<T_shape>) {
128 for (size_t n = 0; n < stan::math::size(alpha); ++n) {
129 partials<1>(ops_partials)[n] *= P;
130 }
131 }
132 if constexpr (is_autodiff_v<T_inv_scale>) {
133 for (size_t n = 0; n < stan::math::size(beta); ++n) {
134 partials<2>(ops_partials)[n] *= P;
135 }
136 }
137 return ops_partials.build(P);
138}
139
140} // namespace math
141} // namespace stan
142#endif
VectorBuilder allocates type T1 values to be used as intermediate values.
scalar_seq_view provides a uniform sequence-like wrapper around either a scalar or a sequence of scal...
return_type_t< T_y, T_shape, T_inv_scale > gamma_cdf(const T_y &y, const T_shape &alpha, const T_inv_scale &beta)
The cumulative density function for a gamma distribution for y with the specified shape and inverse s...
Definition gamma_cdf.hpp:36
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
void check_nonnegative(const char *function, const char *name, const T_y &y)
Check if y is non-negative.
bool size_zero(const T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
Definition size_zero.hpp:19
auto pow(const T1 &x1, const T2 &x2)
Definition pow.hpp:32
void check_consistent_sizes(const char *)
Trivial no input case, this function is a no-op.
return_type_t< T1, T2 > grad_reg_inc_gamma(T1 a, T2 z, T1 g, T1 dig, double precision=1e-6, int max_steps=1e5)
Gradient of the regularized incomplete gamma functions igamma(a, z)
fvar< T > gamma_p(const fvar< T > &x1, const fvar< T > &x2)
Definition gamma_p.hpp:18
fvar< T > tgamma(const fvar< T > &x)
Return the result of applying the gamma function to the specified argument.
Definition tgamma.hpp:21
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.
static constexpr double INFTY
Positive infinity.
Definition constants.hpp:44
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.
Definition digamma.hpp:23
fvar< T > exp(const fvar< T > &x)
Definition exp.hpp:15
typename ref_type_if< true, T >::type ref_type_t
Definition ref_type.hpp:56
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 ...