bdm::EKFfull Class Reference

Extended Kalman Filter in full matrices. More...

#include <libKF.h>

List of all members.

Public Member Functions

 EKFfull ()
 Default constructor.
void set_parameters (diffbifn *pfxu, diffbifn *phxu, const mat Q0, const mat R0)
 Set nonlinear functions for mean values and covariance matrices.
void bayes (const vec &dt)
 Here dt = [yt;ut] of appropriate dimensions.
void set_statistics (vec mu0, mat P0)
 set estimates
const epdfposterior () const
 dummy!
const enorm< fsqmat > * _e () const
const mat _R ()
virtual string to_string ()
 This method returns a basic info about the current instance.
virtual void from_setting (const Setting &root)
 This method arrange instance properties according the data stored in the Setting structure.
virtual void to_setting (Setting &root) 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 epdfepredictor () const
 Constructs a predictive density $ f(d_{t+1} |d_{t}, \ldots d_{0}) $.
virtual mpdfpredictor () const
 Constructs a conditional density 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)

Public Attributes

vec mu
 Mean value of the posterior density.
mat P
 Variance of the posterior density.
bool evalll
double ll

Protected Attributes

diffbifnpfxu
 Internal Model f(x,u).
diffbifnphxu
 Observation Model h(x,u).
enorm< fsqmatE
int dimx
int dimy
int dimu
mat A
mat B
mat C
mat D
mat R
mat Q
mat _Pp
mat _Ry
mat _iRy
mat _K
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.

Friends

std::ostream & operator<< (std::ostream &os, const KalmanFull &kf)
 print elements of KF

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

Extended Kalman Filter in full matrices.

An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean.


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 files:

Generated on Wed Jun 17 14:13:28 2009 for mixpp by  doxygen 1.5.8