| | 516 | //! \brief Gauss-Wishart with recursion on moments |
| | 517 | //! Using precision as parameter |
| | 518 | //! following notation of [Karny Andrysek 2009], precision |
| | 519 | template<class sq_T> |
| | 520 | class egw_ls: public eEF{ |
| | 521 | public: |
| | 522 | vec theta; |
| | 523 | sq_T P; |
| | 524 | double omega; |
| | 525 | double nu; |
| | 526 | |
| | 527 | vec mean() const{ |
| | 528 | return concat(theta, omega); |
| | 529 | } |
| | 530 | mat covariance() const { |
| | 531 | sq_T tmp=P; |
| | 532 | tmp*=nu/((nu-2)*omega); |
| | 533 | return tmp.to_mat();//<======= error - missing omega |
| | 534 | } |
| | 535 | vec variance() const { |
| | 536 | return diag(covariance());//<======= error - missing omega |
| | 537 | } |
| | 538 | vec sample() const NOT_IMPLEMENTED(vec(0)); |
| | 539 | double lognc() const {return 0.0;} //TODO |
| | 540 | }; |
| | 541 | |
