1#ifndef STAN_MATH_PRIM_PROB_SKEW_NORMAL_LCDF_HPP
2#define STAN_MATH_PRIM_PROB_SKEW_NORMAL_LCDF_HPP
27template <
typename T_y,
typename T_loc,
typename T_scale,
typename T_shape>
29 const T_y& y,
const T_loc& mu,
const T_scale& sigma,
const T_shape& alpha) {
35 static constexpr const char* function =
"skew_normal_lcdf";
37 mu,
"Scale parameter", sigma,
"Shape parameter",
41 T_sigma_ref sigma_ref = sigma;
42 T_alpha_ref alpha_ref = alpha;
61 const auto& diff =
to_ref((y_val - mu_val) / sigma_val);
62 const auto& scaled_diff
63 = to_ref_if<!is_constant_all<T_y, T_loc, T_scale>::value>(diff
65 const auto& erfc_m_scaled_diff =
erfc(-scaled_diff);
66 const auto& owens_t_diff_alpha =
owens_t(diff, alpha_val);
68 = to_ref_if<!is_constant_all<T_y, T_loc, T_scale, T_shape>::value>(
69 0.5 * erfc_m_scaled_diff - 2 * owens_t_diff_alpha);
71 T_partials_return cdf_log =
sum(
log(cdf_log_));
74 const auto& diff_square
75 = to_ref_if < !is_constant_all<T_y, T_loc, T_scale>::value
78 const auto& erf_alpha_scaled_diff =
erf(alpha_val * scaled_diff);
79 const auto& exp_m_scaled_diff_square =
exp(-0.5 * diff_square);
80 auto rep_deriv = to_ref_if<!is_constant_all<T_y>::value
85 * exp_m_scaled_diff_square / (sigma_val * cdf_log_));
87 partials<1>(ops_partials) = -rep_deriv;
90 partials<2>(ops_partials) = -rep_deriv * diff;
93 partials<0>(ops_partials) = std::move(rep_deriv);
97 const auto& alpha_square =
square(alpha_val);
98 edge<3>(ops_partials).partials_
99 = -
exp(-0.5 * diff_square * (1.0 + alpha_square))
100 / ((1 + alpha_square) *
pi()) / cdf_log_;
103 return ops_partials.build(cdf_log);
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
bool size_zero(const T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
fvar< T > log(const fvar< T > &x)
fvar< T > erf(const fvar< T > &x)
static constexpr double INV_SQRT_TWO_PI
The value of 1 over the square root of , .
static constexpr double SQRT_TWO
The value of 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...
fvar< T > owens_t(const fvar< T > &x1, const fvar< T > &x2)
Return Owen's T function applied to the specified arguments.
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.
fvar< T > erfc(const fvar< T > &x)
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.
void check_positive(const char *function, const char *name, const T_y &y)
Check if y is positive.
static constexpr double pi()
Return the value of pi.
auto make_partials_propagator(Ops &&... ops)
Construct an partials_propagator.
return_type_t< T_y, T_loc, T_scale, T_shape > skew_normal_lcdf(const T_y &y, const T_loc &mu, const T_scale &sigma, const T_shape &alpha)
fvar< T > square(const fvar< T > &x)
fvar< T > exp(const fvar< T > &x)
typename ref_type_if<!is_constant< T >::value, 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 ...
Extends std::true_type when instantiated with zero or more template parameters, all of which extend t...