Automatic Differentiation
 
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uniform_lpdf.hpp
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1#ifndef STAN_MATH_PRIM_PROB_UNIFORM_LPDF_HPP
2#define STAN_MATH_PRIM_PROB_UNIFORM_LPDF_HPP
3
17#include <cmath>
18
19namespace stan {
20namespace math {
21
44template <bool propto, typename T_y, typename T_low, typename T_high,
46 T_y, T_low, T_high>* = nullptr>
47return_type_t<T_y, T_low, T_high> uniform_lpdf(const T_y& y, const T_low& alpha,
48 const T_high& beta) {
49 using T_partials_return = partials_return_t<T_y, T_low, T_high>;
50 using T_y_ref = ref_type_if_not_constant_t<T_y>;
51 using T_alpha_ref = ref_type_if_not_constant_t<T_low>;
52 using T_beta_ref = ref_type_if_not_constant_t<T_high>;
53 static constexpr const char* function = "uniform_lpdf";
54 check_consistent_sizes(function, "Random variable", y,
55 "Lower bound parameter", alpha,
56 "Upper bound parameter", beta);
57
58 T_y_ref y_ref = y;
59 T_alpha_ref alpha_ref = alpha;
60 T_beta_ref beta_ref = beta;
61
62 decltype(auto) y_val = to_ref(as_value_column_array_or_scalar(y_ref));
63 decltype(auto) alpha_val = to_ref(as_value_column_array_or_scalar(alpha_ref));
64 decltype(auto) beta_val = to_ref(as_value_column_array_or_scalar(beta_ref));
65
66 check_not_nan(function, "Random variable", y_val);
67 check_finite(function, "Lower bound parameter", alpha_val);
68 check_finite(function, "Upper bound parameter", beta_val);
69 check_greater(function, "Upper bound parameter", beta_val, alpha_val);
70
71 if (size_zero(y, alpha, beta)) {
72 return 0.0;
73 }
75 return 0.0;
76 }
77 if (sum(promote_scalar<int>(y_val < alpha_val))
78 || sum(promote_scalar<int>(beta_val < y_val))) {
79 return LOG_ZERO;
80 }
81
82 T_partials_return logp = 0;
83 size_t N = max_size(y, alpha, beta);
85 logp -= sum(log(beta_val - alpha_val)) * N / max_size(alpha, beta);
86 }
87
88 auto ops_partials = make_partials_propagator(y_ref, alpha_ref, beta_ref);
89
91 auto inv_beta_minus_alpha = to_ref_if<(!is_constant_all<T_high>::value
93 inv(beta_val - alpha_val));
97 partials<2>(ops_partials) = -inv_beta_minus_alpha * math::size(y);
98 } else {
99 partials<2>(ops_partials) = -inv_beta_minus_alpha;
100 }
101 }
105 partials<1>(ops_partials) = inv_beta_minus_alpha * math::size(y);
106 } else {
107 partials<1>(ops_partials) = std::move(inv_beta_minus_alpha);
108 }
109 }
110 }
111
112 return ops_partials.build(logp);
113}
114
115template <typename T_y, typename T_low, typename T_high>
117 const T_low& alpha,
118 const T_high& beta) {
119 return uniform_lpdf<false>(y, alpha, beta);
120}
121
122} // namespace math
123} // namespace stan
124#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_low_cl, T_high_cl > uniform_lpdf(const T_y_cl &y, const T_low_cl &alpha, const T_high_cl &beta)
The log of a uniform density for the given y, lower, and upper bound.
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.
static constexpr double LOG_ZERO
The natural logarithm of 0, .
Definition constants.hpp:68
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
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
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.
fvar< T > beta(const fvar< T > &x1, const fvar< T > &x2)
Return fvar with the beta function applied to the specified arguments and its gradient.
Definition beta.hpp:51
void check_greater(const char *function, const char *name, const T_y &y, const T_low &low, Idxs... idxs)
Throw an exception if y is not strictly greater than low.
fvar< T > inv(const fvar< T > &x)
Definition inv.hpp:12
auto make_partials_propagator(Ops &&... ops)
Construct an partials_propagator.
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
If the input type T is either an eigen matrix with 1 column or 1 row at compile time or a standard ve...
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...