#include <mixef.h>


Public Member Functions | |
| MixEF (Array< BMEF * > &Coms0, const vec &alpha0) | |
| Full constructor. | |
| MixEF (BMEF *Com0, const mat &Data, int c=5) | |
| void | bayes (const vec &dt) |
| Recursive EM-like algorithm (QB-variant), see Karny et. al, 2006. | |
| void | bayes (const mat &dt) |
| EM algorithm. | |
| void | bayesB (const mat &dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| double | logpred (const vec &dt) const |
| const epdf & | _epdf () const |
| Returns a pointer to the epdf representing posterior density on parameters. Use with care! | |
| const RV & | _rv () const |
| access function | |
| double | _ll () const |
| access function | |
| void | set_evalll (bool evl0) |
| access function | |
| virtual BM * | _copy_ (bool changerv=false) |
Protected Member Functions | |
| void | build_est () |
| Auxiliary function for use in constructors. | |
Protected Attributes | |
| int | n |
| Number of components. | |
| Array< BMEF * > | Coms |
Models for Components of . | |
| multiBM | weights |
| Statistics for weights. | |
| eprod * | est |
| Posterior on component parameters. | |
| RV | rv |
| Random variable of the posterior. | |
| 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. | |
An approximate estimation method for models with latent discrete variable, such as mixture models of the following kind:
where
is a known function of past outputs,
are component weights, and component parameters
are assumed to be mutually independent.
is an aggregation af all component parameters and weights, i.e.
.
The characteristic feature of this model is that if the exact values of the latent variable were known, estimation of the parameters can be handled by a single model. For example, for the case of mixture models, posterior density for each component parameters would be a BayesianModel from Exponential Family.
This class uses EM-style type algorithms for estimation of its parameters. Under this simplification, the posterior density is a product of exponential family members, hence approximate estimation project this class itself belongs to the exponential family.
TODO: Extend BM to use rvc.
| MixEF::MixEF | ( | BMEF * | Com0, | |
| const mat & | Data, | |||
| int | c = 5 | |||
| ) |
Constructor Initializing the mixture by a random pick of centroids from data
| Com0 | Initial component - necessary to determine its type. | |
| Data | Data on which the initialization will be done | |
| c | Initial number of components, default=5 |
References BM::_copy_(), build_est(), Coms, and n.
| double MixEF::logpred | ( | const vec & | dt | ) | const [virtual] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented from BM.
References multiBM::_epdf(), Coms, epdf::mean(), n, and weights.
| virtual BM* BM::_copy_ | ( | bool | changerv = false |
) | [inline, virtual, inherited] |
1.5.6