bdm::PF Class Reference

Trivial particle filter with proposal density equal to parameter evolution model. More...

#include <libPF.h>

List of all members.

Public Member Functions

void set_options (const string &opt)
void bayes (const vec &dt)
 Incremental Bayes rule.
vec * __w ()
 access function
Constructors
 PF ()
void set_parameters (mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC)
void set_statistics (const vec w0, epdf *epdf0)
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.
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)
virtual const epdfposterior () const =0
virtual const epdf_e () const =0

Protected Attributes

int n
 number of particles;
eEmp est
 posterior density
vec & _w
 pointer into eEmp
Array< vec > & _samples
 pointer into eEmp
mpdfpar
 Parameter evolution model.
mpdfobs
 Observation model.
RESAMPLING_METHOD resmethod
 which resampling method will be used
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.
Options
bool opt_L_smp
 Log all samples.
bool opt_L_wei
 Log all samples.

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 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

Trivial particle filter with proposal density equal to parameter evolution model.

Posterior density is represented by a weighted empirical density (eEmp ).


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.

void bdm::PF::bayes ( const vec &  dt  )  [virtual]

Incremental Bayes rule.

Parameters:
dt vector of input data

Implements bdm::BM.

Reimplemented in bdm::MPF< BM_T >.

References bdm::mpdf::_e(), _samples, _w, est, bdm::epdf::evallog(), bdm::mpdf::evallogcond(), n, obs, par, bdm::eEmp::resample(), resmethod, and bdm::mpdf::samplecond().

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().

void bdm::PF::set_options ( const string &  opt  )  [inline]

Set posterior density by sampling from epdf0

Reimplemented from bdm::BM.

Reimplemented in bdm::MPF< BM_T >.

References opt_L_smp, and opt_L_wei.


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

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