arx.h

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#ifndef AR_H
#define AR_H

#include "../stat/libFN.h"
#include "../stat/libEF.h"

namespace bdm{

class ARX: public BMEF {
protected:
        egiw est;
        ldmat &V;
        double ν
public:
        ARX ( const double frg0=1.0 ) : BMEF ( frg0 ),est (), V ( est._V() ), nu ( est._nu() )
        {last_lognc=est.lognc();};

        ARX ( const ARX &A0 ) : BMEF (),est ( A0.est ), V ( est._V() ), nu ( est._nu() ) {};

        ARX* _copy_();
        
//      //! Set parameters given by moments, \c mu (mean of theta), \c R (mean of R) and \c C (variance of theta)
//      void set_parameters ( const vec &mu, const mat &R, const mat &C, double dfm){};
        void set_parameters ( const ldmat &V0, const double &nu0 )
        {est._V() =V0;est._nu() =nu0;last_lognc=est.lognc();}
        void set_statistics ( const BMEF* BM0 );
        void get_parameters ( mat &V0, double &nu0 ) {V0=est._V().to_mat(); nu0=est._nu();}
        void bayes ( const vec &dt, const double w );
        void bayes ( const vec &dt ) {bayes ( dt,1.0 );};
        const epdf& _epdf() const {return est;}
        double logpred ( const vec &dt ) const;
        void flatten (const BMEF* B ) {
                const ARX* A=dynamic_cast<const ARX*>(B);
                // nu should be equal to B.nu
                est.pow ( A->nu/nu);
                if(evalll){last_lognc=est.lognc();}
        }
        enorm<ldmat>* epredictor( const vec &rgr) const; 
        enorm<ldmat>* epredictor() const {it_assert_debug(rv0.count()==V.rows()-1,"Regressor is not only 1");return epredictor(vec_1(1.0));}
        mlnorm<ldmat>* predictor() const;
        mlstudent* predictor_student() const;
        ivec structure_est ( egiw Eg0 );
        const egiw* _e() const {return &est ;};
};

}

#endif // AR_H

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