bivariate.hpp

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#pragma once
 
#include <randlib/math/constants.hpp>
#include <randlib/math/functions.hpp>
#include <randlib/core/errors.hpp>
#include <randlib/core/distribution.hpp>
 
namespace randlib
{
 
template <class T1, class T2, typename T>
class BivariateDistribution : public ProbabilityDistribution<Pair<T>>
{
protected:
    T1 X{}; 
    T2 Y{}; 
    BivariateDistribution() {}
 
public:
    virtual ~BivariateDistribution() {}
 
protected:
public:
    [[nodiscard]] Pair<T> MinValue() const override { return Pair<T>(X.MinValue(), Y.MinValue()); }
    [[nodiscard]] Pair<T> MaxValue() const override { return Pair<T>(X.MaxValue(), Y.MaxValue()); }
 
    [[nodiscard]] virtual LongDoublePair Mean() const final;
    virtual LongDoubleTriplet Covariance() const final;
    virtual long double Correlation() const = 0;
    [[nodiscard]] virtual std::pair<T1, T2> GetMarginalDistributions() const final;
    [[nodiscard]] virtual Pair<T> Mode() const = 0;
};
 
} // namespace randlib
 
#include <randlib/core/univariate.hpp>
#include <randlib/discrete/binomial.hpp>
#include <randlib/continuous/student_t.hpp>
#include <randlib/continuous/normal.hpp>
#include <randlib/continuous/inverse_gamma.hpp>
 
namespace randlib
{
 
template <class T1, class T2, typename T>
LongDoublePair BivariateDistribution<T1, T2, T>::Mean() const
{
    return std::make_pair(X.Mean(), Y.Mean());
}
 
template <class T1, class T2, typename T>
LongDoubleTriplet BivariateDistribution<T1, T2, T>::Covariance() const
{
    long double var1 = X.Variance();
    long double var2 = Y.Variance();
    long double covariance = (var1 == 0.0L || var2 == 0.0L) ? 0.0L : Correlation() * std::sqrt(var1) * std::sqrt(var2);
    return std::make_tuple(var1, covariance, var2);
}
 
template <class T1, class T2, typename T>
std::pair<T1, T2> BivariateDistribution<T1, T2, T>::GetMarginalDistributions() const
{
    return std::make_pair(X, Y);
}
 
} // namespace randlib