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bdm/stat/libBM.cpp
r192 r201 111 111 } 112 112 113 mat epdf::sample N( int N ) const {113 mat epdf::sample_m ( int N ) const { 114 114 mat X = zeros ( rv.count(), N ); 115 115 for ( int i = 0;i < N;i++ ) X.set_col ( i, this->sample() ); -
bdm/stat/libBM.h
r200 r201 169 169 virtual vec sample () const =0; 170 170 //! Returns N samples from density \f$epdf(rv)\f$ 171 virtual mat sampleN ( int N ) const; 172 //! Compute probability of argument \c val 173 virtual double eval ( const vec &val ) const {return exp ( this->evalpdflog ( val ) );}; 174 171 virtual mat sample_m ( int N ) const; 172 175 173 //! Compute log-probability of argument \c val 176 174 virtual double evalpdflog ( const vec &val ) const =0; … … 232 230 233 231 //! Shortcut for conditioning and evaluation of the internal epdf. In some cases, this operation can be implemented efficiently. 234 virtual double evalcond ( const vec &dt, const vec &cond ) {this->condition ( cond );return ep->eval ( dt );}; 232 virtual double evalcond ( const vec &dt, const vec &cond ) {this->condition ( cond );return exp(ep->evalpdflog ( dt ));}; 233 234 virtual vec evalcond_m ( const mat &Dt, const vec &cond ) {this->condition ( cond );return exp(ep->evalpdflog_m ( Dt ));}; 235 235 236 236 //! Destructor for future use;