bdm::Kalman< sq_T > Class Template Reference

Kalman filter with covariance matrices in square root form. More...

#include <kalman.h>

List of all members.

Public Member Functions

Kalman ()

Default constructor.

Kalman (const Kalman< sq_T > &K0)

Copy constructor.

void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &Q0, const sq_T &R0)

Set parameters with check of relevance.

void set_est (const vec &mu0, const sq_T &P0)

Set estimate values, used e.g. in initialization.

void bayes (const vec &dt)

Here dt = [yt;ut] of appropriate dimensions.

const epdf & posterior () const

access function

mat & __K ()

access function

vec _dP ()

access function

virtual string to_string ()

This method returns a basic info about the current instance.

virtual void from_setting (const Setting &set)

This method arrange instance properties according the data stored in the Setting structure.

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

virtual mpdf * predictor () const

Constructs conditional density of 1-step ahead predictor $f(d_{t+1} |d_{t+h-1}, \ldots d_{t})$ .

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

RV rvy

Indetifier of output rv.

RV rvu

Indetifier of exogeneous rv.

int dimx

cache of rv.count()

int dimy

cache of rvy.count()

int dimu

cache of rvu.count()

mat A

Matrix A.

mat B

Matrix B.

mat C

Matrix C.

mat D

Matrix D.

sq_T Q

Matrix Q in square-root form.

sq_T R

Matrix R in square-root form.

enorm< sq_T > est

posterior density on $x_t$

enorm< sq_T > fy

preditive density on $y_t$

mat _K

placeholder for Kalman gain

vec & _yp

cache of fy.mu

sq_T & _Ry

cache of fy.R

vec & _mu

cache of est.mu

sq_T & _P

cache of est.R

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.

Detailed Description

template<class sq_T>
class bdm::Kalman< sq_T >

Kalman filter with covariance matrices in square root form.

Parameter evolution model:

$x_t = A x_{t-1} + B u_t + Q^{1/2} e_t$

Observation model:

$y_t = C x_{t-1} + C u_t + Q^{1/2} w_t.$

Where $e_t$ and $w_t$ are independent vectors Normal(0,1)-distributed disturbances.

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

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.

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

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

kalman.h


Public Member Functions
	Kalman ()
	Default constructor.
	Kalman (const Kalman< sq_T > &K0)
	Copy constructor.
void	set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &Q0, const sq_T &R0)
	Set parameters with check of relevance.
void	set_est (const vec &mu0, const sq_T &P0)
	Set estimate values, used e.g. in initialization.
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
const epdf &	posterior () const
	access function
mat &	__K ()
	access function
vec	_dP ()
	access function
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	from_setting (const Setting &set)
	This method arrange instance properties according the data stored in the Setting structure.
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 $f(d_{t+1} \|d_{t}, \ldots d_{0})$ .
virtual mpdf *	predictor () const
	Constructs conditional density of 1-step ahead predictor $f(d_{t+1} \|d_{t+h-1}, \ldots d_{t})$ .
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
RV	rvy
	Indetifier of output rv.
RV	rvu
	Indetifier of exogeneous rv.
int	dimx
	cache of rv.count()
int	dimy
	cache of rvy.count()
int	dimu
	cache of rvu.count()
mat	A
	Matrix A.
mat	B
	Matrix B.
mat	C
	Matrix C.
mat	D
	Matrix D.
sq_T	Q
	Matrix Q in square-root form.
sq_T	R
	Matrix R in square-root form.
enorm< sq_T >	est
	posterior density on $x_t$
enorm< sq_T >	fy
	preditive density on $y_t$
mat	_K
	placeholder for Kalman gain
vec &	_yp
	cache of fy.mu
sq_T &	_Ry
	cache of fy.R
vec &	_mu
	cache of est.mu
sq_T &	_P
	cache of est.R
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.

bdm::Kalman< sq_T > Class Template Reference

Public Member Functions

Protected Attributes

Extension to conditional BM

Logging of results

Detailed Description

template<class sq_T> class bdm::Kalman< sq_T >

Member Function Documentation

template<class sq_T>
class bdm::Kalman< sq_T >