Automatic Differentiation
 
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cauchy_lpdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_CAUCHY_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_CAUCHY_LPDF_HPP
3
18#include <cmath>
19
20namespace stan {
21namespace math {
22
40template <bool propto, typename T_y, typename T_loc, typename T_scale,
42 T_y, T_loc, T_scale>* = nullptr>
44 const T_loc& mu,
45 const T_scale& sigma) {
46 using T_partials_return = partials_return_t<T_y, T_loc, T_scale>;
47 using std::log;
48 using T_y_ref = ref_type_if_not_constant_t<T_y>;
49 using T_mu_ref = ref_type_if_not_constant_t<T_loc>;
50 using T_sigma_ref = ref_type_if_not_constant_t<T_scale>;
51 static constexpr const char* function = "cauchy_lpdf";
52 check_consistent_sizes(function, "Random variable", y, "Location parameter",
53 mu, "Scale parameter", sigma);
54 T_y_ref y_ref = y;
55 T_mu_ref mu_ref = mu;
56 T_sigma_ref sigma_ref = sigma;
57
58 if (size_zero(y, mu, sigma)) {
59 return 0.0;
60 }
62 return 0.0;
63 }
64
65 T_partials_return logp(0.0);
66 auto ops_partials = make_partials_propagator(y_ref, mu_ref, sigma_ref);
67
68 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
69 decltype(auto) mu_val = to_ref(as_value_column_array_or_scalar(mu_ref));
70 decltype(auto) sigma_val = to_ref(as_value_column_array_or_scalar(sigma_ref));
71 check_not_nan(function, "Random variable", y_val);
72 check_finite(function, "Location parameter", mu_val);
73 check_positive_finite(function, "Scale parameter", sigma_val);
74
75 size_t N = max_size(y, mu, sigma);
76
77 const auto& inv_sigma = to_ref_if<is_autodiff_v<T_scale>>(inv(sigma_val));
78 const auto& y_minus_mu
79 = to_ref_if<is_any_autodiff_v<T_y, T_loc, T_scale>>(y_val - mu_val);
80
81 logp -= sum(log1p(square(y_minus_mu * inv_sigma)));
82 if constexpr (include_summand<propto>::value) {
83 logp -= N * LOG_PI;
84 }
86 logp -= sum(log(sigma_val)) * N / math::size(sigma);
87 }
88
89 if constexpr (is_any_autodiff_v<T_y, T_loc, T_scale>) {
90 const auto& sigma_squared
91 = to_ref_if<is_autodiff_v<T_scale>>(square(sigma_val));
92 const auto& y_minus_mu_squared
93 = to_ref_if<is_autodiff_v<T_scale>>(square(y_minus_mu));
94 if constexpr (is_any_autodiff_v<T_y, T_loc>) {
95 auto mu_deriv = to_ref_if<(is_autodiff_v<T_y> && is_autodiff_v<T_loc>)>(
96 2 * y_minus_mu / (sigma_squared + y_minus_mu_squared));
97 if constexpr (is_autodiff_v<T_y>) {
98 if constexpr (is_vector<T_y>::value) {
99 partials<0>(ops_partials) = -mu_deriv;
100 } else {
101 partials<0>(ops_partials)[0] = -sum(mu_deriv);
102 }
103 }
104 if constexpr (is_autodiff_v<T_loc>) {
105 partials<1>(ops_partials) = std::move(mu_deriv);
106 }
107 }
108 if constexpr (is_autodiff_v<T_scale>) {
109 partials<2>(ops_partials) = (y_minus_mu_squared - sigma_squared)
110 * inv_sigma
111 / (sigma_squared + y_minus_mu_squared);
112 }
113 }
114 return ops_partials.build(logp);
115}
116
117template <typename T_y, typename T_loc, typename T_scale>
119 const T_loc& mu,
120 const T_scale& sigma) {
121 return cauchy_lpdf<false>(y, mu, sigma);
122}
123
124} // namespace math
125} // namespace stan
126#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_loc_cl, T_scale_cl > cauchy_lpdf(const T_y_cl &y, const T_loc_cl &mu, const T_scale_cl &sigma)
The log of the Cauchy density for the specified scalar(s) given the specified location parameter(s) a...
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
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:45
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.
static constexpr double LOG_PI
The natural logarithm of , .
Definition constants.hpp:86
fvar< T > log1p(const fvar< T > &x)
Definition log1p.hpp:12
void check_finite(const char *function, const char *name, const T_y &y)
Return true if all values in y are finite.
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
ref_type_t< T && > to_ref(T &&a)
This evaluates expensive Eigen expressions.
Definition to_ref.hpp:18
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_size.hpp:20
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 > square(const fvar< T > &x)
Definition square.hpp:12
typename ref_type_if< is_autodiff_v< T >, T >::type ref_type_if_not_constant_t
Definition ref_type.hpp:63
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 ...
If the input type T is either an eigen matrix with 1 column or 1 row at compile time or a standard ve...
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...