35 | | //Test estimation |
36 | | int ndat = 100; // number of data records |
37 | | vec Yt ( ndat ); // Store generated data |
38 | | Yt.set_subvector ( 0, randn ( ord ) ); //initial values |
39 | | vec rgr ( ord ); // regressor |
| 35 | //Test estimation |
| 36 | int ndat = 100; // number of data records |
| 37 | vec Yt ( ndat ); // Store generated data |
| 38 | Yt.set_subvector ( 0, randn ( ord ) ); //initial values |
| 39 | vec rgr ( ord ); // regressor |
56 | | // Build predictor |
57 | | mlstudent* Pr = Ar.predictor_student ( ); |
58 | | // Test similarity of likelihoods from the Bayes rule and the predictor |
59 | | cout << "BR log-lik: " << Ar._ll(); |
60 | | cout << " , predictor ll: " << Pr->evallogcond ( vec_1 ( Yt ( t ) ), rgr ) << endl; |
61 | | delete Pr; |
62 | | } |
63 | | //print posterior moments |
64 | | cout << "posterior mean: " << f_thr.mean() << endl; |
65 | | cout << "posterior variance: " << f_thr.variance() << endl; |
| 56 | // Build predictor |
| 57 | mlstudent* Pr = Ar.predictor_student ( ); |
| 58 | // Test similarity of likelihoods from the Bayes rule and the predictor |
| 59 | cout << "BR log-lik: " << Ar._ll(); |
| 60 | cout << " , predictor ll: " << Pr->evallogcond ( vec_1 ( Yt ( t ) ), rgr ) << endl; |
| 61 | delete Pr; |
| 62 | } |
| 63 | //print posterior moments |
| 64 | cout << "posterior mean: " << f_thr.mean() << endl; |
| 65 | cout << "posterior variance: " << f_thr.variance() << endl; |