exponential.hpp

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#ifndef __STAN__PROB__DISTRIBUTIONS__UNIVARIATE__CONTINUOUS__EXPONENTIAL_HPP__
#define __STAN__PROB__DISTRIBUTIONS__UNIVARIATE__CONTINUOUS__EXPONENTIAL_HPP__
 
#include <stan/agrad.hpp>
#include <stan/math/error_handling.hpp>
#include <stan/math/special_functions.hpp>
#include <stan/meta/traits.hpp>
#include <stan/prob/constants.hpp>
#include <stan/prob/traits.hpp>
 
namespace stan {
  
  namespace prob {
 
    template <bool propto,
              typename T_y, typename T_inv_scale, 
              class Policy>
    typename return_type<T_y,T_inv_scale>::type
    exponential_log(const T_y& y, const T_inv_scale& beta, 
                    const Policy&) {
      static const char* function = "stan::prob::exponential_log(%1%)";
 
      // check if any vectors are zero length
      if (!(stan::length(y) 
            && stan::length(beta)))
        return 0.0;
      
      using stan::math::check_finite;
      using stan::math::check_positive;
      using stan::math::check_not_nan;
      using stan::math::check_consistent_sizes;
      
      typename return_type<T_y,T_inv_scale>::type logp(0.0);
      if(!check_not_nan(function, y, "Random variable", &logp, Policy()))
        return logp;
      if(!check_finite(function, beta, "Inverse scale parameter", &logp, Policy()))
        return logp;
      if(!check_positive(function, beta, "Inverse scale parameter", &logp, Policy()))
        return logp;
 
      if (!(check_consistent_sizes(function,
                                   y,beta,
                                   "Random variable","Inverse scale parameter",
                                   &logp, Policy())))
        return logp;
      
      
      // set up template expressions wrapping scalars into vector views
      VectorView<const T_y> y_vec(y);
      VectorView<const T_inv_scale> beta_vec(beta);
      size_t N = max_size(y, beta);
      
      for (size_t n = 0; n < N; n++) {
        if (include_summand<propto,T_inv_scale>::value)
          logp += log(beta_vec[n]);
        if (include_summand<propto,T_y,T_inv_scale>::value)
          logp -= beta_vec[n] * y_vec[n];
      }
      return logp;
    }
    
    template <bool propto,
              typename T_y, typename T_inv_scale>
    inline
    typename return_type<T_y,T_inv_scale>::type
    exponential_log(const T_y& y, const T_inv_scale& beta) {
      return exponential_log<propto>(y,beta,stan::math::default_policy());
    }
 
    template <typename T_y, typename T_inv_scale,
              class Policy>
    inline
    typename return_type<T_y,T_inv_scale>::type
    exponential_log(const T_y& y, const T_inv_scale& beta, const Policy&) {
      return exponential_log<false>(y,beta,Policy());
    }
 
    template <typename T_y, typename T_inv_scale>
    inline
    typename return_type<T_y,T_inv_scale>::type
    exponential_log(const T_y& y, const T_inv_scale& beta) {
      return exponential_log<false>(y,beta,stan::math::default_policy());
    }
 
 
 
    template <typename T_y, 
              typename T_inv_scale, 
              class Policy>
    typename boost::math::tools::promote_args<T_y,T_inv_scale>::type
    exponential_cdf(const T_y& y, 
                  const T_inv_scale& beta, 
                  const Policy&) {
 
      static const char* function = "stan::prob::exponential_cdf(%1%)";
 
      using stan::math::check_finite;
      using stan::math::check_positive;
      using stan::math::check_not_nan;
      using boost::math::tools::promote_args;
 
      typename promote_args<T_y,T_inv_scale>::type lp;
      if(!check_not_nan(function, y, "Random variable", &lp, Policy()))
        return lp;
      if(!check_finite(function, beta, "Inverse scale parameter", &lp, Policy()))
        return lp;
      if(!check_positive(function, beta, "Inverse scale parameter", &lp, Policy()))
        return lp;
      
      if (y < 0)
        return 1.0;
 
      return 1.0 - exp(-beta * y);
    }
 
    template <typename T_y, 
              typename T_inv_scale>
    inline
    typename boost::math::tools::promote_args<T_y,T_inv_scale>::type
    exponential_cdf(const T_y& y, 
                  const T_inv_scale& beta) {
      return exponential_cdf(y,beta,stan::math::default_policy());
    }
    
 
 
  }
}
 
#endif