Automatic Differentiation
 
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inv_gamma_lpdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_INV_GAMMA_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_INV_GAMMA_LPDF_HPP
3
20#include <cmath>
21
22namespace stan {
23namespace math {
24
41template <bool propto, typename T_y, typename T_shape, typename T_scale,
43 T_y, T_shape, T_scale>* = nullptr>
45 const T_shape& alpha,
46 const T_scale& beta) {
47 using T_partials_return = partials_return_t<T_y, T_shape, T_scale>;
48 using T_y_ref = ref_type_if_not_constant_t<T_y>;
49 using T_alpha_ref = ref_type_if_not_constant_t<T_shape>;
50 using T_beta_ref = ref_type_if_not_constant_t<T_scale>;
51 static constexpr const char* function = "inv_gamma_lpdf";
52 check_consistent_sizes(function, "Random variable", y, "Shape parameter",
53 alpha, "Scale parameter", beta);
54
55 T_y_ref y_ref = y;
56 T_alpha_ref alpha_ref = alpha;
57 T_beta_ref beta_ref = beta;
58
59 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
60 decltype(auto) alpha_val = to_ref(as_value_column_array_or_scalar(alpha_ref));
61 decltype(auto) beta_val = to_ref(as_value_column_array_or_scalar(beta_ref));
62
63 check_not_nan(function, "Random variable", y_val);
64 check_positive_finite(function, "Shape parameter", alpha_val);
65 check_positive_finite(function, "Scale parameter", beta_val);
66
67 if (size_zero(y, alpha, beta)) {
68 return 0;
69 }
71 return 0;
72 }
73 if (sum(promote_scalar<int>(y_val <= 0))) {
74 return LOG_ZERO;
75 }
76
77 T_partials_return logp(0);
78 auto ops_partials = make_partials_propagator(y_ref, alpha_ref, beta_ref);
79
80 const auto& log_y
81 = to_ref_if<include_summand<propto, T_y, T_shape>::value>(log(y_val));
82
83 size_t N = max_size(y, alpha, beta);
85 logp -= sum(lgamma(alpha_val)) * N / math::size(alpha);
86 }
88 const auto& log_beta
89 = to_ref_if<!is_constant_all<T_shape>::value>(log(beta_val));
90 logp += sum(alpha_val * log_beta) * N / max_size(alpha, beta);
92 partials<1>(ops_partials) = log_beta - digamma(alpha_val) - log_y;
93 }
94 }
96 logp -= sum((alpha_val + 1.0) * log_y) * N / max_size(y, alpha);
97 }
99 const auto& inv_y
102 logp -= sum(beta_val * inv_y) * N / max_size(y, beta);
104 edge<0>(ops_partials).partials_
105 = (beta_val * inv_y - alpha_val - 1) * inv_y;
106 }
108 partials<2>(ops_partials) = alpha_val / beta_val - inv_y;
109 }
110 }
111 return ops_partials.build(logp);
112}
113
114template <typename T_y, typename T_shape, typename T_scale>
116 const T_y& y, const T_shape& alpha, const T_scale& beta) {
117 return inv_gamma_lpdf<false>(y, alpha, beta);
118}
119
120} // namespace math
121} // namespace stan
122#endif
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_scale_cl > inv_gamma_lpdf(const T_y_cl &y, const T_shape_cl &alpha, const T_scale_cl &beta)
The log of an inverse gamma density for y with the specified shape and 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
T to_ref_if(T &&a)
No-op that should be optimized away.
Definition to_ref.hpp:29
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
void check_not_nan(const char *function, const char *name, const T_y &y)
Check if y is not NaN.
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
fvar< T > inv(const fvar< T > &x)
Definition inv.hpp:13
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...