MixEF Class Reference

Mixture of Exponential Family Densities. More...

#include <mixef.h>

Inheritance diagram for MixEF:

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Collaboration diagram for MixEF:

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List of all members.

Public Member Functions
	MixEF (const Array< BMEF * > &Coms0, const vec &alpha0)
	Full constructor.
void	init (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!
emix *	predictor (const RV &rv)
	Constructs a predictive density (marginal density on data).
void	flatten (double sumw=1.0)
	Flatten the density as if it was not estimated from the data.
vec	logpred_m (const mat &dt) const
	Matrix version of logpred.
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.

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Detailed Description

Mixture of Exponential Family Densities.

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 under EM-style approximate estimation this class itself belongs to the exponential family.

TODO: Extend BM to use rvc.

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Member Function Documentation

void MixEF::init	(	BMEF *	Com0,
		const mat &	Data,
		int	c = 5
	)

Initializing the mixture by a random pick of centroids from data

Parameters:

	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, est, n, multiBM::set_parameters(), and weights.

Referenced by merger::merge().

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.

Referenced by merger::evalpdflog(), and merger::merge().

virtual BM* BM::_copy_

(

bool

changerv = false

)

[inline, virtual, inherited]

Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype: BM* _copy_(){BM Tmp*=new Tmp(this*); return Tmp; }

Reimplemented in ARX.

Referenced by init().

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The documentation for this class was generated from the following files:

• work/git/mixpp/bdm/estim/mixef.h
• work/git/mixpp/bdm/estim/mixef.cpp