bdm::EKFfull Class Reference

Extended Kalman Filter in full matrices. More...

#include <kalman.h>

List of all members.

Public Member Functions

 EKFfull ()
 Default constructor.
void set_parameters (const shared_ptr< diffbifn > &pfxu, const shared_ptr< 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 (const vec &mu0, const mat &P0)
 set estimates
const mat _R ()
void set_statistics (const vec &mu0, const fsqmat &P0)
const enorm< fsqmat > & posterior () const
 posterior
shared_ptr< epdfshared_posterior ()
 shared posterior
void from_setting (const Setting &set)
 load basic elements of Kalman from structure
void validate ()
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.
void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const fsqmat &Q0, const fsqmat &R0)
int _dimx ()
 access function
int _dimy ()
 access function
int _dimu ()
 access function
const mat & _A () const
 access function
const mat & _B () const
 access function
const mat & _C () const
 access function
const mat & _D () const
 access function
const fsqmat_Q () const
 access function
const fsqmat_R () const
 access function
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 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

shared_ptr< diffbifnpfxu
 Internal Model f(x,u).
shared_ptr< diffbifnphxu
 Observation Model h(x,u).
RV yrv
 id of output
RV urv
 id of input
mat _K
 Kalman gain.
shared_ptr< enorm< fsqmat > > est
 posterior
enorm< fsqmatfy
 marginal on data f(y|y)
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.
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.
fsqmat Q
 Matrix Q in square-root form.
fsqmat R
 Matrix R in square-root form.

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::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.

References bdm_error.

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


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

Generated on Sun Sep 13 22:40:43 2009 for mixpp by  doxygen 1.6.1