1#ifndef STAN_MATH_PRIM_PROB_SKEW_DOUBLE_EXPONENTIAL_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_SKEW_DOUBLE_EXPONENTIAL_LPDF_HPP
39template <
bool propto,
typename T_y,
typename T_loc,
typename T_scale,
42 T_y, T_loc, T_scale, T_skewness>* =
nullptr>
43inline return_type_t<T_y, T_loc, T_scale, T_skewness>
45 const T_scale& sigma,
const T_skewness& tau) {
51 static constexpr const char* function =
"skew_double_exponential_lpdf";
53 mu,
"Shape parameter", sigma,
"Skewness parameter",
58 T_sigma_ref sigma_ref = sigma;
59 T_tau_ref tau_ref = tau;
80 check_bounded(function,
"Skewness parameter", tau_val, 0.0, 1.0);
82 const auto& inv_sigma = to_ref_if<is_autodiff_v<T_scale>>(
inv(sigma_val));
83 const auto& y_m_mu = to_ref_if<is_any_autodiff_v<T_y, T_loc>>(y_val - mu_val);
84 const auto& diff_sign =
sign(y_m_mu);
86 const auto& diff_sign_smaller_0 =
step(-diff_sign);
87 const auto& abs_diff_y_mu =
fabs(y_m_mu);
88 const auto& abs_diff_y_mu_over_sigma = abs_diff_y_mu * inv_sigma;
89 const auto& expo = to_ref_if<is_autodiff_v<T_skewness>>(
90 (diff_sign_smaller_0 + diff_sign * tau_val) * abs_diff_y_mu_over_sigma);
92 size_t N =
max_size(y, mu, sigma, tau);
93 T_partials_return logp = -2.0 *
sum(expo);
105 if constexpr (is_any_autodiff_v<T_y, T_loc>) {
106 const auto& deriv =
to_ref_if<(is_autodiff_v<T_y> && is_autodiff_v<T_loc>)>(
107 2.0 * (diff_sign_smaller_0 + diff_sign * tau_val) * diff_sign
109 if constexpr (is_autodiff_v<T_y>) {
110 partials<0>(ops_partials) = -deriv;
112 if constexpr (is_autodiff_v<T_loc>) {
113 partials<1>(ops_partials) = deriv;
116 if constexpr (is_autodiff_v<T_scale>) {
117 partials<2>(ops_partials) = -inv_sigma + 2.0 * expo * inv_sigma;
119 if constexpr (is_autodiff_v<T_skewness>) {
120 edge<3>(ops_partials).partials_
121 =
inv(tau_val) -
inv(1.0 - tau_val)
122 + (-1.0 * diff_sign) * 2.0 * abs_diff_y_mu_over_sigma;
125 return ops_partials.build(logp);
128template <
typename T_y,
typename T_loc,
typename T_scale,
typename T_skewness>
131 const T_scale& sigma,
const T_skewness& tau) {
132 return skew_double_exponential_lpdf<false>(y, mu, sigma, tau);
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>>.
bool size_zero(const T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
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.
T to_ref_if(T &&a)
No-op that should be optimized away.
T step(const T &y)
The step, or Heaviside, function.
fvar< T > log(const fvar< T > &x)
static constexpr double LOG_TWO
The natural logarithm 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.
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.
ref_type_t< T && > to_ref(T &&a)
This evaluates expensive Eigen expressions.
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
fvar< T > log1m(const fvar< T > &x)
fvar< T > inv(const fvar< T > &x)
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)
typename ref_type_if< is_autodiff_v< T >, T >::type ref_type_if_not_constant_t
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 ...
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...