bdm::EKFCh Class Reference

Extended Kalman Filter in Square root. More...


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.

#include <kalman.h>

List of all members.

Public Member Functions

BM_copy_ () const
 copy constructor duplicated - calls different set_parameters
void set_parameters (const shared_ptr< diffbifn > &pfxu, const shared_ptr< 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 from_setting (const Setting &set)
 load basic elements of Kalman from structure
void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0)
 set parameters for adapt from Kalman
void initialize ()
 initialize internal parametetrs
void set_statistics (const vec &mu0, const mat &P0)
void set_statistics (const vec &mu0, const chmat &P0)
const enorm< chmat > & posterior () const
 posterior
shared_ptr< epdfshared_posterior ()
 shared posterior
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.
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 chmat_Q () const
 access function
const chmat_R () const
 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 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< chmat > > est
 posterior
enorm< chmatfy
 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.
chmat Q
 Matrix Q in square-root form.
chmat R
 Matrix R in square-root form.
Internal storage - needs initialize()

pre array (triangular matrix)



mat preA
mat postA
 post array (triangular matrix)

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.

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.

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 23:08:56 2009 for mixpp by  doxygen 1.6.1