Automatic Differentiation
 
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skew_normal_lpdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_SKEW_NORMAL_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_SKEW_NORMAL_LPDF_HPP
3
20#include <cmath>
21
22namespace stan {
23namespace math {
24
25template <bool propto, typename T_y, typename T_loc, typename T_scale,
26 typename T_shape,
28 T_y, T_loc, T_scale, T_shape>* = nullptr>
30 const T_y& y, const T_loc& mu, const T_scale& sigma, const T_shape& alpha) {
32 using T_y_ref = ref_type_if_not_constant_t<T_y>;
33 using T_mu_ref = ref_type_if_not_constant_t<T_loc>;
34 using T_sigma_ref = ref_type_if_not_constant_t<T_scale>;
35 using T_alpha_ref = ref_type_if_not_constant_t<T_shape>;
36 static constexpr const char* function = "skew_normal_lpdf";
37 check_consistent_sizes(function, "Random variable", y, "Location parameter",
38 mu, "Scale parameter", sigma, "Shape paramter", alpha);
39 T_y_ref y_ref = y;
40 T_mu_ref mu_ref = mu;
41 T_sigma_ref sigma_ref = sigma;
42 T_alpha_ref alpha_ref = alpha;
43
44 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
45 decltype(auto) mu_val = to_ref(as_value_column_array_or_scalar(mu_ref));
46 decltype(auto) sigma_val = to_ref(as_value_column_array_or_scalar(sigma_ref));
47 decltype(auto) alpha_val = to_ref(as_value_column_array_or_scalar(alpha_ref));
48
49 check_not_nan(function, "Random variable", y_val);
50 check_finite(function, "Location parameter", mu_val);
51 check_finite(function, "Shape parameter", alpha_val);
52 check_positive(function, "Scale parameter", sigma_val);
53
54 if (size_zero(y, mu, sigma, alpha)) {
55 return 0.0;
56 }
58 return 0.0;
59 }
60
61 auto ops_partials
62 = make_partials_propagator(y_ref, mu_ref, sigma_ref, alpha_ref);
63
64 const auto& inv_sigma
65 = to_ref_if<!is_constant_all<T_y, T_loc, T_scale>::value>(inv(sigma_val));
66 const auto& y_minus_mu_over_sigma
67 = to_ref_if<include_summand<propto, T_y, T_loc, T_scale, T_shape>::value>(
68 (y_val - mu_val) * inv_sigma);
69 const auto& log_erfc_alpha_z
70 = to_ref_if<!is_constant_all<T_y, T_loc, T_scale, T_shape>::value>(
71 log(erfc(-alpha_val * y_minus_mu_over_sigma * INV_SQRT_TWO)));
72
73 size_t N = max_size(y, mu, sigma, alpha);
74 T_partials_return logp = sum(log_erfc_alpha_z);
76 logp -= HALF_LOG_TWO_PI * N;
77 }
79 logp -= sum(log(sigma_val)) * N / math::size(sigma);
80 }
82 logp -= sum(square(y_minus_mu_over_sigma)) * 0.5 * N
83 / max_size(y, mu, sigma);
84 }
85
87 const auto& sq = square(alpha_val * y_minus_mu_over_sigma * INV_SQRT_TWO);
88 const auto& ex = exp(-sq - log_erfc_alpha_z);
89 auto deriv_logerf = to_ref_if<!is_constant_all<T_y, T_loc>::value
92 >= 2>(SQRT_TWO_OVER_SQRT_PI * ex);
94 auto deriv_y_loc = to_ref_if<(!is_constant_all<T_y>::value
96 (y_minus_mu_over_sigma - deriv_logerf * alpha_val) * inv_sigma);
98 partials<0>(ops_partials) = -deriv_y_loc;
99 }
101 partials<1>(ops_partials) = std::move(deriv_y_loc);
102 }
103 }
105 edge<2>(ops_partials).partials_
106 = ((y_minus_mu_over_sigma - deriv_logerf * alpha_val)
107 * y_minus_mu_over_sigma
108 - 1)
109 * inv_sigma;
110 }
112 partials<3>(ops_partials) = deriv_logerf * y_minus_mu_over_sigma;
113 }
114 }
115 return ops_partials.build(logp);
116}
117
118template <typename T_y, typename T_loc, typename T_scale, typename T_shape>
120 const T_y& y, const T_loc& mu, const T_scale& sigma, const T_shape& alpha) {
121 return skew_normal_lpdf<false>(y, mu, sigma, alpha);
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, T_shape_cl > skew_normal_lpdf(const T_y_cl &y, const T_loc_cl &mu, const T_scale_cl &sigma, const T_shape_cl &alpha)
The log of the skew normal density for the specified scalar(s) given the specified mean(s),...
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 SQRT_TWO_OVER_SQRT_PI
The square root of 2 divided by the square root of , .
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
static constexpr double INV_SQRT_TWO
The value of 1 over the square root of 2, .
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 > erfc(const fvar< T > &x)
Definition erfc.hpp:16
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
void check_positive(const char *function, const char *name, const T_y &y)
Check if y is positive.
static constexpr double HALF_LOG_TWO_PI
The value of half the natural logarithm , .
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.
fvar< T > square(const fvar< T > &x)
Definition square.hpp:12
fvar< T > exp(const fvar< T > &x)
Definition exp.hpp:15
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...