bdm::multiBM Class Reference

Estimator for Multinomial density. More...

---

Detailed Description

Estimator for Multinomial density.

#include <exp_family.h>

List of all members.

Public Member Functions
	multiBM ()
	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)
const eDirich &	posterior () const
	reimplemnetation of BM::posterior()
void	set_parameters (const vec &beta0)
	constructor function
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).
virtual void	flatten (const BMEF *B)
	Flatten the posterior according to the given BMEF (of the same type!).
BMEF *	_copy_ () const
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.
Mathematical operations
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 *	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
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	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.

---

Member Function Documentation

BMEF* bdm::BMEF::_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 from bdm::BM.

Reimplemented in bdm::ARX.

References bdm_error.

Referenced by bdm::MixEF::init().

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:

• exp_family.h