1#ifndef STAN_MATH_PRIM_PROB_WEIBULL_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_WEIBULL_LPDF_HPP
37template <
bool propto,
typename T_y,
typename T_shape,
typename T_scale,
39 T_y, T_shape, T_scale>* =
nullptr>
42 const T_scale& sigma) {
48 static constexpr const char* function =
"weibull_lpdf";
50 alpha,
"Scale parameter", sigma);
53 T_alpha_ref alpha_ref = alpha;
54 T_sigma_ref sigma_ref = sigma;
73 if (
sum(promote_scalar<int>(y_val < 0))) {
78 = to_ref_if<include_summand<propto, T_y, T_shape>::value>(
log(y_val));
80 = to_ref_if<include_summand<propto, T_shape, T_scale>::value>(
83 = to_ref_if<!is_constant_all<T_scale>::value>(
inv(sigma_val));
84 const auto& y_div_sigma_pow_alpha
85 = to_ref_if<!is_constant_all<T_y, T_shape, T_scale>::value>(
86 pow(y_val * inv_sigma, alpha_val));
88 size_t N =
max_size(y, alpha, sigma);
89 T_partials_return logp = -
sum(y_div_sigma_pow_alpha);
94 logp +=
sum((alpha_val - 1.0) * log_y) * N /
max_size(alpha, y);
97 logp -=
sum(alpha_val * log_sigma) * N /
max_size(alpha, sigma);
101 edge<0>(ops_partials).partials_
102 = (alpha_val * (1 - y_div_sigma_pow_alpha) - 1.0) / y_val;
105 edge<1>(ops_partials).partials_
106 =
inv(alpha_val) + (1.0 - y_div_sigma_pow_alpha) * (log_y - log_sigma);
109 edge<2>(ops_partials).partials_
110 = alpha_val * inv_sigma * (y_div_sigma_pow_alpha - 1.0);
112 return ops_partials.build(logp);
115template <
typename T_y,
typename T_shape,
typename T_scale>
117 const T_shape& alpha,
118 const T_scale& sigma) {
119 return weibull_lpdf<false>(y, alpha, sigma);
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_shape_cl, T_scale_cl > weibull_lpdf(const T_y_cl &y, const T_shape_cl &alpha, const T_scale_cl &sigma)
Returns the Weibull log probability density for the given location and scale.
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>>.
static constexpr double LOG_ZERO
The natural logarithm of 0, .
bool size_zero(const T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
auto pow(const T1 &x1, const T2 &x2)
fvar< T > log(const fvar< T > &x)
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.
void check_finite(const char *function, const char *name, const T_y &y)
Return true if all values in y are finite.
auto sum(const std::vector< T > &m)
Return the sum of the entries of the specified standard vector.
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
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.
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...
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...