Automatic Differentiation
 
Loading...
Searching...
No Matches
skew_double_exponential_lpdf.hpp
Go to the documentation of this file.
1#ifndef STAN_MATH_PRIM_PROB_SKEW_DOUBLE_EXPONENTIAL_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_SKEW_DOUBLE_EXPONENTIAL_LPDF_HPP
3
17#include <cmath>
18
19namespace stan {
20namespace math {
21
39template <bool propto, typename T_y, typename T_loc, typename T_scale,
40 typename T_skewness,
42 T_y, T_loc, T_scale, T_skewness>* = nullptr>
44 const T_y& y, const T_loc& mu, const T_scale& sigma,
45 const T_skewness& tau) {
47 using T_y_ref = ref_type_if_not_constant_t<T_y>;
48 using T_mu_ref = ref_type_if_not_constant_t<T_loc>;
49 using T_sigma_ref = ref_type_if_not_constant_t<T_scale>;
51 static constexpr const char* function = "skew_double_exponential_lpdf";
52 check_consistent_sizes(function, "Random variable", y, "Location parameter",
53 mu, "Shape parameter", sigma, "Skewness parameter",
54 tau);
55
56 T_y_ref y_ref = y;
57 T_mu_ref mu_ref = mu;
58 T_sigma_ref sigma_ref = sigma;
59 T_tau_ref tau_ref = tau;
60
61 if (size_zero(y, mu, sigma, tau)) {
62 return 0.0;
63 }
65 return 0.0;
66 }
67
68 auto ops_partials
69 = make_partials_propagator(y_ref, mu_ref, sigma_ref, tau_ref);
70
71 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
72 decltype(auto) mu_val = to_ref(as_value_column_array_or_scalar(mu_ref));
73 decltype(auto) sigma_val = to_ref(as_value_column_array_or_scalar(sigma_ref));
74 decltype(auto) tau_val = to_ref(as_value_column_array_or_scalar(tau_ref));
75
76 check_not_nan(function, "Random variable", y_val);
77 check_finite(function, "Location parameter", mu_val);
78 check_positive_finite(function, "Scale parameter", sigma_val);
79 check_bounded(function, "Skewness parameter", tau_val, 0.0, 1.0);
80
81 const auto& inv_sigma
82 = to_ref_if<!is_constant_all<T_scale>::value>(inv(sigma_val));
83 const auto& y_m_mu
84 = to_ref_if<!is_constant_all<T_y, T_loc>::value>(y_val - mu_val);
85 const auto& diff_sign = sign(y_m_mu);
86
87 const auto& diff_sign_smaller_0 = step(-diff_sign);
88 const auto& abs_diff_y_mu = fabs(y_m_mu);
89 const auto& abs_diff_y_mu_over_sigma = abs_diff_y_mu * inv_sigma;
90 const auto& expo = to_ref_if<!is_constant_all<T_skewness>::value>(
91 (diff_sign_smaller_0 + diff_sign * tau_val) * abs_diff_y_mu_over_sigma);
92
93 size_t N = max_size(y, mu, sigma, tau);
94 T_partials_return logp = -2.0 * sum(expo);
95
97 logp += N * LOG_TWO;
98 }
100 logp -= sum(log(sigma_val)) * N / math::size(sigma);
101 }
103 logp += sum(log(tau_val) + log1m(tau_val)) * N / math::size(tau);
104 }
105
107 const auto& deriv = to_ref_if<(!is_constant_all<T_y>::value
109 2.0 * (diff_sign_smaller_0 + diff_sign * tau_val) * diff_sign
110 * inv_sigma);
112 partials<0>(ops_partials) = -deriv;
113 }
115 partials<1>(ops_partials) = deriv;
116 }
117 }
119 partials<2>(ops_partials) = -inv_sigma + 2.0 * expo * inv_sigma;
120 }
122 edge<3>(ops_partials).partials_
123 = inv(tau_val) - inv(1.0 - tau_val)
124 + (-1.0 * diff_sign) * 2.0 * abs_diff_y_mu_over_sigma;
125 }
126
127 return ops_partials.build(logp);
128}
129
130template <typename T_y, typename T_loc, typename T_scale, typename T_skewness>
132 const T_y& y, const T_loc& mu, const T_scale& sigma,
133 const T_skewness& tau) {
134 return skew_double_exponential_lpdf<false>(y, mu, sigma, tau);
135}
136
137} // namespace math
138} // namespace stan
139#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_skewness_cl > skew_double_exponential_lpdf(const T_y_cl &y, const T_loc_cl &mu, const T_scale_cl &sigma, const T_skewness_cl &tau)
Returns the log PMF of the skew double exponential distribution.
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
void check_bounded(const char *function, const char *name, const T_y &y, const T_low &low, const T_high &high)
Check if the value is between the low and high values, inclusively.
auto sign(const T &x)
Returns signs of the arguments.
Definition sign.hpp:18
T to_ref_if(T &&a)
No-op that should be optimized away.
Definition to_ref.hpp:29
T step(const T &y)
The step, or Heaviside, function.
Definition step.hpp:31
fvar< T > log(const fvar< T > &x)
Definition log.hpp:15
static constexpr double LOG_TWO
The natural logarithm of 2, .
Definition constants.hpp:80
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
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
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_size.hpp:20
fvar< T > log1m(const fvar< T > &x)
Definition log1m.hpp:12
fvar< T > inv(const fvar< T > &x)
Definition inv.hpp:12
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 > fabs(const fvar< T > &x)
Definition fabs.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...