bdm::EKFCh Class Reference

Extended Kalman Filter in Square root. More...

#include <libKF.h>

List of all members.

Public Member Functions

BM_copy_ () const
 copy constructor duplicated - calls different set_parameters
void set_parameters (diffbifn *pfxu, diffbifn *phxu, const chmat Q0, const chmat R0)
 Set nonlinear functions for mean values and covariance matrices.
void bayes (const vec &dt)
 Here dt = [yt;ut] of appropriate dimensions.
void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0)
 Set parameters with check of relevance.
void set_statistics (const vec &mu0, const chmat &P0)
void set_est (const vec &mu0, const chmat &P0)
 Set estimate values, used e.g. in initialization.
const epdfposterior () const
 access function
const enorm< chmat > * _e () const
mat & __K ()
 access function
vec _dP ()
 access function
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.
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

diffbifnpfxu
 Internal Model f(x,u).
diffbifnphxu
 Observation Model h(x,u).
mat preA
 pre array (triangular matrix)
mat postA
 post array (triangular matrix)
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.
chmat Q
 Matrix Q in square-root form.
chmat R
 Matrix R in square-root form.
enorm< chmatest
 posterior density on $x_t$
enorm< chmatfy
 preditive density on $y_t$
mat _K
 placeholder for Kalman gain
vec & _yp
 cache of fy.mu
chmat_Ry
 cache of fy.R
vec & _mu
 cache of est.mu
chmat_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

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)
ivec LIDs
 IDs of storages in loggers.
bool opt_L_bounds
 Option for logging bounds.


Detailed Description

Extended Kalman Filter in Square root.

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


Member Function Documentation

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:

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