Automatic Differentiation
 
Loading...
Searching...
No Matches
gamma_lccdf.hpp
Go to the documentation of this file.
1#ifndef STAN_MATH_PRIM_PROB_GAMMA_LCCDF_HPP
2#define STAN_MATH_PRIM_PROB_GAMMA_LCCDF_HPP
3
19#include <cmath>
20
21namespace stan {
22namespace math {
23
24template <typename T_y, typename T_shape, typename T_inv_scale>
26 const T_y& y, const T_shape& alpha, const T_inv_scale& beta) {
27 using T_partials_return = partials_return_t<T_y, T_shape, T_inv_scale>;
28 using std::exp;
29 using std::log;
30 using std::pow;
31 using T_y_ref = ref_type_t<T_y>;
32 using T_alpha_ref = ref_type_t<T_shape>;
33 using T_beta_ref = ref_type_t<T_inv_scale>;
34 static constexpr const char* function = "gamma_lccdf";
35 check_consistent_sizes(function, "Random variable", y, "Shape parameter",
36 alpha, "Inverse scale parameter", beta);
37 T_y_ref y_ref = y;
38 T_alpha_ref alpha_ref = alpha;
39 T_beta_ref beta_ref = beta;
40 check_positive_finite(function, "Shape parameter", alpha_ref);
41 check_positive_finite(function, "Inverse scale parameter", beta_ref);
42 check_nonnegative(function, "Random variable", y_ref);
43
44 if (size_zero(y, alpha, beta)) {
45 return 0;
46 }
47
48 T_partials_return P(0.0);
49 auto ops_partials = make_partials_propagator(y_ref, alpha_ref, beta_ref);
50
51 scalar_seq_view<T_y_ref> y_vec(y_ref);
52 scalar_seq_view<T_alpha_ref> alpha_vec(alpha_ref);
53 scalar_seq_view<T_beta_ref> beta_vec(beta_ref);
54 size_t N = max_size(y, alpha, beta);
55
56 // Explicit return for extreme values
57 // The gradients are technically ill-defined, but treated as zero
58 for (size_t i = 0; i < stan::math::size(y); i++) {
59 if (y_vec.val(i) == 0) {
60 // LCCDF(0) = log(P(Y > 0)) = log(1) = 0
61 return ops_partials.build(0.0);
62 }
63 }
64
65 for (size_t n = 0; n < N; n++) {
66 // Explicit results for extreme values
67 // The gradients are technically ill-defined, but treated as zero
68 if (y_vec.val(n) == INFTY) {
69 // LCCDF(∞) = log(P(Y > ∞)) = log(0) = -∞
70 return ops_partials.build(negative_infinity());
71 }
72
73 const T_partials_return y_dbl = y_vec.val(n);
74 const T_partials_return alpha_dbl = alpha_vec.val(n);
75 const T_partials_return beta_dbl = beta_vec.val(n);
76 const T_partials_return beta_y_dbl = beta_dbl * y_dbl;
77
78 // Qn = 1 - Pn
79 const T_partials_return Qn = gamma_q(alpha_dbl, beta_y_dbl);
80 const T_partials_return log_Qn = log(Qn);
81
82 P += log_Qn;
83
84 if constexpr (is_any_autodiff_v<T_y, T_inv_scale>) {
85 const T_partials_return log_y_dbl = log(y_dbl);
86 const T_partials_return log_beta_dbl = log(beta_dbl);
87 const T_partials_return log_pdf
88 = alpha_dbl * log_beta_dbl - lgamma(alpha_dbl)
89 + (alpha_dbl - 1.0) * log_y_dbl - beta_y_dbl;
90 const T_partials_return common_term = exp(log_pdf - log_Qn);
91
92 if constexpr (is_autodiff_v<T_y>) {
93 // d/dy log(1-F(y)) = -f(y)/(1-F(y))
94 partials<0>(ops_partials)[n] -= common_term;
95 }
96 if constexpr (is_autodiff_v<T_inv_scale>) {
97 // d/dbeta log(1-F(y)) = -y*f(y)/(beta*(1-F(y)))
98 partials<2>(ops_partials)[n] -= y_dbl / beta_dbl * common_term;
99 }
100 }
101
102 if constexpr (is_autodiff_v<T_shape>) {
103 const T_partials_return digamma_val = digamma(alpha_dbl);
104 const T_partials_return gamma_val = tgamma(alpha_dbl);
105 // d/dalpha log(1-F(y)) = grad_upper_inc_gamma / (1-F(y))
106 partials<1>(ops_partials)[n]
107 += grad_reg_inc_gamma(alpha_dbl, beta_y_dbl, gamma_val, digamma_val)
108 / Qn;
109 }
110 }
111 return ops_partials.build(P);
112}
113
114} // namespace math
115} // namespace stan
116
117#endif
scalar_seq_view provides a uniform sequence-like wrapper around either a scalar or a sequence of scal...
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 negative_infinity()
Return negative infinity.
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
return_type_t< T_y, T_shape, T_inv_scale > gamma_lccdf(const T_y &y, const T_shape &alpha, const T_inv_scale &beta)
fvar< T > log(const fvar< T > &x)
Definition log.hpp:18
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 > lgamma(const fvar< T > &x)
Return the natural logarithm of the gamma function applied to the specified argument.
Definition lgamma.hpp:21
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 > gamma_q(const fvar< T > &x1, const fvar< T > &x2)
Definition gamma_q.hpp:19
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:46
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 ...