Automatic Differentiation
 
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pareto_type_2_lpdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_PARETO_TYPE_2_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_PARETO_TYPE_2_LPDF_HPP
3
17#include <cmath>
18
19namespace stan {
20namespace math {
21
22// pareto_type_2(y|lambda, alpha) [y >= 0; lambda > 0; alpha > 0]
23template <bool propto, typename T_y, typename T_loc, typename T_scale,
24 typename T_shape,
26 T_y, T_loc, T_scale, T_shape>* = nullptr>
28 const T_y& y, const T_loc& mu, const T_scale& lambda,
29 const T_shape& alpha) {
31 using T_y_ref = ref_type_if_not_constant_t<T_y>;
32 using T_mu_ref = ref_type_if_not_constant_t<T_loc>;
33 using T_lambda_ref = ref_type_if_not_constant_t<T_scale>;
34 using T_alpha_ref = ref_type_if_not_constant_t<T_shape>;
35 static constexpr const char* function = "pareto_type_2_lpdf";
36 check_consistent_sizes(function, "Random variable", y, "Location parameter",
37 mu, "Scale parameter", lambda, "Shape parameter",
38 alpha);
39
40 if (size_zero(y, mu, lambda, alpha)) {
41 return 0.0;
42 }
43
44 T_y_ref y_ref = y;
45 T_mu_ref mu_ref = mu;
46 T_lambda_ref lambda_ref = lambda;
47 T_alpha_ref alpha_ref = alpha;
48
49 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
50 decltype(auto) mu_val = to_ref(as_value_column_array_or_scalar(mu_ref));
51 decltype(auto) lambda_val
53 decltype(auto) alpha_val = to_ref(as_value_column_array_or_scalar(alpha_ref));
54
55 check_greater_or_equal(function, "Random variable", y_val, mu_val);
56 check_positive_finite(function, "Scale parameter", lambda_val);
57 check_positive_finite(function, "Shape parameter", alpha_val);
58
60 return 0.0;
61 }
62
63 const auto& log1p_scaled_diff = to_ref_if<!is_constant_all<T_shape>::value>(
64 log1p((y_val - mu_val) / lambda_val));
65
66 size_t N = max_size(y, mu, lambda, alpha);
67 T_partials_return logp(0.0);
69 logp += sum(log(alpha_val)) * N / math::size(alpha);
70 }
72 logp -= sum(log(lambda_val)) * N / math::size(lambda);
73 }
75 logp -= sum((alpha_val + 1.0) * log1p_scaled_diff);
76 }
77
78 auto ops_partials
79 = make_partials_propagator(y_ref, mu_ref, lambda_ref, alpha_ref);
80
82 const auto& inv_sum = to_ref_if<(!is_constant_all<T_y, T_loc>::value
84 inv(lambda_val + y_val - mu_val));
85 const auto& alpha_div_sum
87 && !is_constant_all<T_scale>::value)>(alpha_val * inv_sum);
89 auto deriv_1_2 = to_ref_if<(!is_constant_all<T_y>::value
91 inv_sum + alpha_div_sum);
93 partials<0>(ops_partials) = -deriv_1_2;
94 }
96 partials<1>(ops_partials) = std::move(deriv_1_2);
97 }
98 }
100 edge<2>(ops_partials).partials_
101 = alpha_div_sum * (y_val - mu_val) / lambda_val - inv_sum;
102 }
103 }
105 partials<3>(ops_partials) = inv(alpha_val) - log1p_scaled_diff;
106 }
107
108 return ops_partials.build(logp);
109}
110
111template <typename T_y, typename T_loc, typename T_scale, typename T_shape>
113 const T_y& y, const T_loc& mu, const T_scale& lambda,
114 const T_shape& alpha) {
115 return pareto_type_2_lpdf<false>(y, mu, lambda, alpha);
116}
117
118} // namespace math
119} // namespace stan
120#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 > pareto_type_2_lpdf(const T_y_cl &y, const T_loc_cl &mu, const T_scale_cl &lambda, const T_shape_cl &alpha)
Returns the log PMF of the Pareto type 2 distribution.
size_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:18
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
size_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_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
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:15
void check_greater_or_equal(const char *function, const char *name, const T_y &y, const T_low &low, Idxs... idxs)
Throw an exception if y is not greater or equal than low.
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.
fvar< T > sum(const std::vector< fvar< T > > &m)
Return the sum of the entries of the specified standard vector.
Definition sum.hpp:22
ref_type_t< T && > to_ref(T &&a)
This evaluates expensive Eigen expressions.
Definition to_ref.hpp:17
fvar< T > log1p(const fvar< T > &x)
Definition log1p.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.
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 ...
Definition fvar.hpp:9
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...