bdm::multiBM Class Reference
Estimator for Multinomial density. More...
#include <libEF.h>
Inheritance diagram for bdm::multiBM:
Collaboration diagram for bdm::multiBM:
Public Member Functions | |
| multiBM (const RV &rv, const vec beta0) | |
| Default constructor. | |
| multiBM (const multiBM &B) | |
| Copy constructor. | |
| void | set_statistics (const BM *mB0) |
| Sets sufficient statistics to match that of givefrom mB0. | |
| void | bayes (const vec &dt) |
| Incremental Bayes rule. | |
| double | logpred (const vec &dt) const |
| void | flatten (const BMEF *B) |
| Flatten the posterior according to the given BMEF (of the same type!). | |
| const epdf & | _epdf () const |
| Returns a reference to the epdf representing posterior density on parameters. | |
| const eDirich * | _e () const |
| Returns a pointer to the epdf representing posterior density on parameters. Use with care! | |
| void | set_parameters (const vec &beta0) |
| virtual void | set_statistics (const BMEF *BM0) |
| get statistics from another model | |
| virtual void | bayes (const vec &data, const double w) |
| Weighted update of sufficient statistics (Bayes rule). | |
| BMEF * | _copy_ (bool changerv=false) |
| Flatten the posterior as if to keep nu0 data. | |
| virtual void | bayesB (const mat &Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| vec | logpred_m (const mat &dt) const |
| Matrix version of logpred. | |
| virtual epdf * | predictor (const RV &rv) const |
| Constructs a predictive density (marginal density on data). | |
| const RV & | _rv () const |
| access function | |
| const RV & | _drv () const |
| access function | |
| void | set_drv (const RV &rv) |
| set drv | |
| double | _ll () const |
| access function | |
| void | set_evalll (bool evl0) |
| access function | |
Protected Attributes | |
| eDirich | est |
| Conjugate prior and posterior. | |
| vec & | beta |
| Pointer inside est to sufficient statistics. | |
| double | frg |
| forgetting factor | |
| double | last_lognc |
cached value of lognc() in the previous step (used in evaluation of ll ) | |
| RV | rv |
| Random variable of the posterior. | |
| RV | drv |
| Random variable of the data (optional). | |
| double | ll |
| Logarithm of marginalized data likelihood. | |
| bool | evalll |
If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save computational time. | |
Detailed Description
Estimator for Multinomial density.Member Function Documentation
| void bdm::multiBM::bayes | ( | const vec & | dt | ) | [inline, virtual] |
Incremental Bayes rule.
- Parameters:
-
dt vector of input data
Reimplemented from bdm::BMEF.
References beta, est, bdm::BM::evalll, bdm::BMEF::frg, bdm::BMEF::last_lognc, bdm::BM::ll, and bdm::eDirich::lognc().
| double bdm::multiBM::logpred | ( | const vec & | dt | ) | const [inline, virtual] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented from bdm::BM.
References bdm::eDirich::_beta(), beta, est, bdm::BM::evalll, bdm::BMEF::frg, bdm::BMEF::last_lognc, and bdm::eDirich::lognc().
The documentation for this class was generated from the following file:
