bdm::BM Class Reference

Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities. More...

#include <libBM.h>

List of all members.

Public Member Functions

Constructors

BM ()

BM (const BM &B)

virtual BM * _copy_ () const

Mathematical operations

virtual void bayes (const vec &dt)=0

Incremental Bayes rule.

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 $f(d_{t+1} |d_{t}, \ldots d_{0})$ .

virtual mpdf * predictor () const

Constructs a conditional density 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)

virtual const epdf & posterior () const =0

virtual const epdf * _e () const =0

Protected Attributes

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

RV rvc

Name of extension variable.

const RV & _rvc () const

access function

virtual void condition (const vec &val)

Substitute val for rvc.

Logging of results

ivec LIDs

IDs of storages in loggers.

bool opt_L_bounds

Option for logging bounds.

void set_options (const string &opt)

Set boolean options from a string.

virtual void log_add (logger &L, const string &name="")

Add all logged variables to a logger.

virtual void logit (logger &L)

Detailed Description

Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.

This object represents exact or approximate evaluation of the Bayes rule:

$f(\theta_t | d_1,\ldots,d_t) = \frac{f(y_t|\theta_t,\cdot) f(\theta_t|d_1,\ldots,d_{t-1})}{f(y_t|d_1,\ldots,d_{t-1})}$

Access to the resulting posterior density is via function posterior().

As a "side-effect" it also evaluates log-likelihood of the data, which can be accessed via function _ll(). It can also evaluate predictors of future values of $y_t$ , see functions epredictor() and predictor().

Alternatively, it can evaluate posterior density conditioned by a known constant, $ c_t $ :

$f(\theta_t | c_t, d_1,\ldots,d_t) \propto f(y_t,\theta_t|c_t,\cdot, d_1,\ldots,d_{t-1})$

The value of $ c_t $ is set by function condition().

Member Function Documentation

virtual BM* bdm::BM::_copy_ ( ) const [inline, virtual]

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::EKF< sq_T >, bdm::EKFCh, and bdm::BMEF.

virtual void bdm::BM::bayes ( const vec & dt ) [pure virtual]

Incremental Bayes rule.

Parameters:

dt

vector of input data

Implemented in pmsmCRB, EKFfixed, bdm::ARX, bdm::Kalman< sq_T >, bdm::KalmanCh, bdm::EKFfull, bdm::EKF< sq_T >, bdm::EKFCh, bdm::PF, bdm::MPF< BM_T >, bdm::MixEF, bdm::BMEF, bdm::multiBM, bdm::Kalman< chmat >, bdm::Kalman< ldmat >, and bdm::Kalman< fsqmat >.

Referenced by bayesB().

virtual double bdm::BM::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 in bdm::ARX, bdm::MixEF, and bdm::multiBM.

Referenced by logpred_m().

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

libBM.h
libBM.cpp


Public Member Functions
Constructors
	BM ()
	BM (const BM &B)
virtual BM *	_copy_ () const
Mathematical operations
virtual void	bayes (const vec &dt)=0
	Incremental Bayes rule.
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 $f(d_{t+1} \|d_{t}, \ldots d_{0})$ .
virtual mpdf *	predictor () const
	Constructs a conditional density 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)
virtual const epdf &	posterior () const =0
virtual const epdf *	_e () const =0
Protected Attributes
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
RV	rvc
	Name of extension variable.
const RV &	_rvc () const
	access function
virtual void	condition (const vec &val)
	Substitute `val` for `rvc`.
Logging of results
ivec	LIDs
	IDs of storages in loggers.
bool	opt_L_bounds
	Option for logging bounds.
void	set_options (const string &opt)
	Set boolean options from a string.
virtual void	log_add (logger &L, const string &name="")
	Add all logged variables to a logger.
virtual void	logit (logger &L)