Stan Math Library  2.20.0
reverse mode automatic differentiation
uniform_lpdf.hpp
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1 #ifndef STAN_MATH_PRIM_SCAL_PROB_UNIFORM_LPDF_HPP
2 #define STAN_MATH_PRIM_SCAL_PROB_UNIFORM_LPDF_HPP
3 
12 #include <cmath>
13 
14 namespace stan {
15 namespace math {
16 
39 template <bool propto, typename T_y, typename T_low, typename T_high>
41  const T_y& y, const T_low& alpha, const T_high& beta) {
42  static const char* function = "uniform_lpdf";
44  T_partials_return;
45 
46  using std::log;
47 
48  if (size_zero(y, alpha, beta))
49  return 0.0;
50 
51  T_partials_return logp(0.0);
52  check_not_nan(function, "Random variable", y);
53  check_finite(function, "Lower bound parameter", alpha);
54  check_finite(function, "Upper bound parameter", beta);
55  check_greater(function, "Upper bound parameter", beta, alpha);
56  check_consistent_sizes(function, "Random variable", y,
57  "Lower bound parameter", alpha,
58  "Upper bound parameter", beta);
59 
61  return 0.0;
62 
63  scalar_seq_view<T_y> y_vec(y);
64  scalar_seq_view<T_low> alpha_vec(alpha);
65  scalar_seq_view<T_high> beta_vec(beta);
66  size_t N = max_size(y, alpha, beta);
67 
68  for (size_t n = 0; n < N; n++) {
69  const T_partials_return y_dbl = value_of(y_vec[n]);
70  if (y_dbl < value_of(alpha_vec[n]) || y_dbl > value_of(beta_vec[n]))
71  return LOG_ZERO;
72  }
73 
75  T_partials_return, T_low, T_high>
76  inv_beta_minus_alpha(max_size(alpha, beta));
77  for (size_t i = 0; i < max_size(alpha, beta); i++)
79  inv_beta_minus_alpha[i]
80  = 1.0 / (value_of(beta_vec[i]) - value_of(alpha_vec[i]));
81 
83  T_partials_return, T_low, T_high>
84  log_beta_minus_alpha(max_size(alpha, beta));
85  for (size_t i = 0; i < max_size(alpha, beta); i++)
87  log_beta_minus_alpha[i]
88  = log(value_of(beta_vec[i]) - value_of(alpha_vec[i]));
89 
90  operands_and_partials<T_y, T_low, T_high> ops_partials(y, alpha, beta);
91  for (size_t n = 0; n < N; n++) {
93  logp -= log_beta_minus_alpha[n];
94 
96  ops_partials.edge2_.partials_[n] += inv_beta_minus_alpha[n];
98  ops_partials.edge3_.partials_[n] -= inv_beta_minus_alpha[n];
99  }
100  return ops_partials.build(logp);
101 }
102 
103 template <typename T_y, typename T_low, typename T_high>
105  const T_y& y, const T_low& alpha, const T_high& beta) {
106  return uniform_lpdf<false>(y, alpha, beta);
107 }
108 
109 } // namespace math
110 } // namespace stan
111 #endif
void check_finite(const char *function, const char *name, const T_y &y)
Check if y is finite.
boost::math::tools::promote_args< double, typename partials_type< typename scalar_type< T >::type >::type, typename partials_return_type< T_pack... >::type >::type type
return_type< T_y, T_low, T_high >::type uniform_lpdf(const T_y &y, const T_low &alpha, const T_high &beta)
The log of a uniform density for the given y, lower, and upper bound.
T value_of(const fvar< T > &v)
Return the value of the specified variable.
Definition: value_of.hpp:17
Extends std::true_type when instantiated with zero or more template parameters, all of which extend t...
Definition: conjunction.hpp:14
fvar< T > log(const fvar< T > &x)
Definition: log.hpp:12
scalar_seq_view provides a uniform sequence-like wrapper around either a scalar or a sequence of scal...
This template builds partial derivatives with respect to a set of operands.
bool size_zero(T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
Definition: size_zero.hpp:18
const double LOG_ZERO
Definition: constants.hpp:150
Template metaprogram to calculate whether a summand needs to be included in a proportional (log) prob...
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
boost::math::tools::promote_args< double, typename scalar_type< T >::type, typename return_type< Types_pack... >::type >::type type
Definition: return_type.hpp:36
void check_not_nan(const char *function, const char *name, const T_y &y)
Check if y is not NaN.
size_t max_size(const T1 &x1, const T2 &x2)
Definition: max_size.hpp:9
T_return_type build(double value)
Build the node to be stored on the autodiff graph.
VectorBuilder allocates type T1 values to be used as intermediate values.
internal::ops_partials_edge< double, Op2 > edge2_
void check_greater(const char *function, const char *name, const T_y &y, const T_low &low)
Check if y is strictly greater than low.
void check_consistent_sizes(const char *function, const char *name1, const T1 &x1, const char *name2, const T2 &x2)
Check if the dimension of x1 is consistent with x2.
internal::ops_partials_edge< double, Op3 > edge3_

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