Automatic Differentiation
 
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neg_binomial_rng.hpp
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1#ifndef STAN_MATH_PRIM_PROB_NEG_BINOMIAL_RNG_HPP
2#define STAN_MATH_PRIM_PROB_NEG_BINOMIAL_RNG_HPP
3
10#include <boost/random/gamma_distribution.hpp>
11#include <boost/random/poisson_distribution.hpp>
12#include <boost/random/variate_generator.hpp>
13
14namespace stan {
15namespace math {
16
35template <typename T_shape, typename T_inv, class RNG>
37 const T_shape& alpha, const T_inv& beta, RNG& rng) {
38 using boost::gamma_distribution;
39 using boost::variate_generator;
40 using boost::random::poisson_distribution;
41 using T_alpha_ref = ref_type_t<T_shape>;
42 using T_beta_ref = ref_type_t<T_inv>;
43 static constexpr const char* function = "neg_binomial_rng";
44 check_consistent_sizes(function, "Shape parameter", alpha,
45 "Inverse scale Parameter", beta);
46 T_alpha_ref alpha_ref = alpha;
47 T_beta_ref beta_ref = beta;
48
49 if (size_zero(alpha, beta)) {
50 return {};
51 }
52
53 check_positive_finite(function, "Shape parameter", alpha_ref);
54 check_positive_finite(function, "Inverse scale parameter", beta_ref);
55
56 scalar_seq_view<T_alpha_ref> alpha_vec(alpha_ref);
57 scalar_seq_view<T_beta_ref> beta_vec(beta_ref);
58 size_t N = max_size(alpha, beta);
60
61 for (size_t n = 0; n < N; ++n) {
62 double rng_from_gamma = variate_generator<RNG&, gamma_distribution<> >(
63 rng, gamma_distribution<>(alpha_vec[n], 1.0 / beta_vec[n]))();
64
65 // same as the constraints for poisson_rng
66 check_less(function, "Random number that came from gamma distribution",
67 rng_from_gamma, POISSON_MAX_RATE);
68 check_not_nan(function, "Random number that came from gamma distribution",
69 rng_from_gamma);
70 check_nonnegative(function,
71 "Random number that came from gamma distribution",
72 rng_from_gamma);
73
74 output[n] = variate_generator<RNG&, poisson_distribution<> >(
75 rng, poisson_distribution<>(rng_from_gamma))();
76 }
77
78 return output.data();
79}
80
81} // namespace math
82} // namespace stan
83#endif
typename helper::type type
VectorBuilder allocates type T1 values to be used as intermediate values.
scalar_seq_view provides a uniform sequence-like wrapper around either a scalar or a sequence of scal...
VectorBuilder< true, int, T_shape, T_inv >::type neg_binomial_rng(const T_shape &alpha, const T_inv &beta, RNG &rng)
Return a negative binomial random variate with the specified shape and inverse scale parameters using...
void check_nonnegative(const char *function, const char *name, const T_y &y)
Check if y is non-negative.
bool size_zero(const T &x)
Returns 1 if input is of length 0, returns 0 otherwise.
Definition size_zero.hpp:19
const double POISSON_MAX_RATE
Largest rate parameter allowed in Poisson RNG.
void check_consistent_sizes(const char *)
Trivial no input case, this function is a no-op.
void check_not_nan(const char *function, const char *name, const T_y &y)
Check if y is not NaN.
void check_less(const char *function, const char *name, const T_y &y, const T_high &high, Idxs... idxs)
Throw an exception if y is not strictly less than high.
int64_t max_size(const T1 &x1, const Ts &... xs)
Calculate the size of the largest input.
Definition max_size.hpp:20
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_positive_finite(const char *function, const char *name, const T_y &y)
Check if y is positive and finite.
typename ref_type_if< true, T >::type ref_type_t
Definition ref_type.hpp:56
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...