chi_square_lccdf.hpp

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#ifndef STAN_MATH_PRIM_PROB_CHI_SQUARE_LCCDF_HPP
#define STAN_MATH_PRIM_PROB_CHI_SQUARE_LCCDF_HPP
 
#include <stan/math/prim/meta.hpp>
#include <stan/math/prim/err.hpp>
#include <stan/math/prim/fun/constants.hpp>
#include <stan/math/prim/fun/digamma.hpp>
#include <stan/math/prim/fun/exp.hpp>
#include <stan/math/prim/fun/gamma_q.hpp>
#include <stan/math/prim/fun/grad_reg_inc_gamma.hpp>
#include <stan/math/prim/fun/log.hpp>
#include <stan/math/prim/fun/max_size.hpp>
#include <stan/math/prim/fun/scalar_seq_view.hpp>
#include <stan/math/prim/fun/size.hpp>
#include <stan/math/prim/fun/size_zero.hpp>
#include <stan/math/prim/fun/tgamma.hpp>
#include <stan/math/prim/fun/value_of.hpp>
#include <stan/math/prim/functor/partials_propagator.hpp>
#include <cmath>
 
namespace stan {
namespace math {
 
template <typename T_y, typename T_dof>
inline return_type_t<T_y, T_dof> chi_square_lccdf(const T_y& y,
                                                  const T_dof& nu) {
  using T_partials_return = partials_return_t<T_y, T_dof>;
  using std::exp;
  using std::log;
  using std::pow;
  using T_y_ref = ref_type_t<T_y>;
  using T_nu_ref = ref_type_t<T_dof>;
  static constexpr const char* function = "chi_square_lccdf";
  check_consistent_sizes(function, "Random variable", y,
                         "Degrees of freedom parameter", nu);
  T_y_ref y_ref = y;
  T_nu_ref nu_ref = nu;
  check_not_nan(function, "Random variable", y_ref);
  check_nonnegative(function, "Random variable", y_ref);
  check_positive_finite(function, "Degrees of freedom parameter", nu_ref);
 
  if (size_zero(y, nu)) {
    return 0;
  }
 
  T_partials_return ccdf_log(0.0);
  auto ops_partials = make_partials_propagator(y_ref, nu_ref);
 
  scalar_seq_view<T_y_ref> y_vec(y_ref);
  scalar_seq_view<T_nu_ref> nu_vec(nu_ref);
  size_t N = max_size(y, nu);
 
  // Explicit return for extreme values
  // The gradients are technically ill-defined, but treated as zero
  for (size_t i = 0; i < stan::math::size(y); i++) {
    if (y_vec.val(i) == 0) {
      return ops_partials.build(0.0);
    }
  }
 
  VectorBuilder<is_autodiff_v<T_dof>, T_partials_return, T_dof> gamma_vec(
      math::size(nu));
  VectorBuilder<is_autodiff_v<T_dof>, T_partials_return, T_dof> digamma_vec(
      math::size(nu));
 
  if constexpr (is_autodiff_v<T_dof>) {
    for (size_t i = 0; i < stan::math::size(nu); i++) {
      const T_partials_return alpha_dbl = nu_vec.val(i) * 0.5;
      gamma_vec[i] = tgamma(alpha_dbl);
      digamma_vec[i] = digamma(alpha_dbl);
    }
  }
 
  for (size_t n = 0; n < N; n++) {
    // Explicit results for extreme values
    // The gradients are technically ill-defined, but treated as zero
    if (y_vec.val(n) == INFTY) {
      return ops_partials.build(negative_infinity());
    }
 
    const T_partials_return y_dbl = y_vec.val(n);
    const T_partials_return alpha_dbl = nu_vec.val(n) * 0.5;
    const T_partials_return beta_dbl = 0.5;
 
    const T_partials_return Pn = gamma_q(alpha_dbl, beta_dbl * y_dbl);
 
    ccdf_log += log(Pn);
 
    if constexpr (is_autodiff_v<T_y>) {
      partials<0>(ops_partials)[n] -= beta_dbl * exp(-beta_dbl * y_dbl)
                                      * pow(beta_dbl * y_dbl, alpha_dbl - 1)
                                      / tgamma(alpha_dbl) / Pn;
    }
    if constexpr (is_autodiff_v<T_dof>) {
      partials<1>(ops_partials)[n]
          += 0.5
             * grad_reg_inc_gamma(alpha_dbl, beta_dbl * y_dbl, gamma_vec[n],
                                  digamma_vec[n])
             / Pn;
    }
  }
  return ops_partials.build(ccdf_log);
}
 
}  // namespace math
}  // namespace stan
#endif