bdm::MultiModel Class Reference

(Switching) Multiple Model The model runs several models in parallel and evaluates thier weights (fittness). More...

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Detailed Description

(Switching) Multiple Model The model runs several models in parallel and evaluates thier weights (fittness).

The statistics of the resulting density are merged using (geometric?) combination.

The next step is performed with the new statistics for all models.

#include <kalman.h>

List of all members.

Public Member Functions
void	set_parameters (Array< EKFCh * > A, int pol0=1)
void	bayes (const vec &dt)
	Incremental Bayes rule.
const enorm< chmat > &	posterior () const
	posterior density
void	from_setting (const Setting &set)
	This method arrange instance properties according the data stored in the Setting structure.
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
virtual void	validate ()
	This method TODO.
Constructors
virtual BM *	_copy_ () const
Mathematical operations
virtual void	bayesB (const mat &Dt)
	Batch Bayes rule (columns of Dt are observations).
virtual double	logpred (const vec &dt) const
vec	logpred_m (const mat &dt) const
	Matrix version of logpred.
virtual epdf *	epredictor () const
	Constructs a predictive density .
virtual mpdf *	predictor () const
	Constructs conditional density of 1-step ahead predictor .
Access to attributes
const RV &	_drv () const
void	set_drv (const RV &rv)
void	set_rv (const RV &rv)
double	_ll () const
void	set_evalll (bool evl0)
Protected Attributes
Array< EKFCh * >	Models
	List of models between which we switch.
vec	w
	vector of model weights
vec	_lls
	cache of model lls
int	policy
	type of switching policy [1=maximum,2=...]
enorm< chmat >	est
	internal statistics
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.
Extension to conditional BM
This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed
const RV &	_rvc () const
	access function
virtual void	condition (const vec &val)
	Substitute val for rvc.
RV	rvc
	Name of extension variable.
Logging of results
virtual void	set_options (const string &opt)
	Set boolean options from a string, recognized are: "logbounds,logll".
virtual void	log_add (logger &L, const string &name="")
	Add all logged variables to a logger.
virtual void	logit (logger &L)
ivec	LIDs
	IDs of storages in loggers 4:[1=mean,2=lb,3=ub,4=ll].
ivec	LFlags
	Flags for logging - same size as LIDs, each entry correspond to the same in LIDs.

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Member Function Documentation

virtual BM* bdm::BM::_copy_

(

)

const [inline, virtual, inherited]

Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:

 BM* _copy_() const {return new BM(*this);} 

Reimplemented in bdm::ARX, bdm::KalmanCh, bdm::EKFCh, and bdm::BMEF.

void bdm::MultiModel::bayes

(

const vec &

dt

)

[inline, virtual]

Incremental Bayes rule.

Parameters:

dt

vector of input data

Implements bdm::BM.

References _lls, bdm_error, est, bdm::enorm< sq_T >::mean(), Models, policy, and w.

virtual double bdm::BM::logpred

(

const vec &

dt

)

const [inline, virtual, inherited]

Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.

Reimplemented in bdm::ARX, bdm::MixEF, and bdm::multiBM.

References bdm_error.

Referenced by bdm::BM::logpred_m().

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

• kalman.h
• kalman.cpp