bdm::PF Class Reference

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

Detailed Description

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

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

#include <particles.h>

List of all members.

Public Member Functions

void set_options (const string &opt)
virtual void bayes_gensmp ()
 bayes I - generate samples and add their weights to lls
virtual void bayes_weights ()
 bayes II - compute weights of the
virtual bool do_resampling ()
 important part of particle filtering - decide if it is time to perform resampling
void bayes (const vec &dt)
 Incremental Bayes rule.
vec & __w ()
 access function
vec & _lls ()
 access function
RESAMPLING_METHOD _resmethod () const
const eEmp & posterior () const
 access function
void from_setting (const Setting &set)
void resmethod_from_set (const Setting &set)
 auxiliary function reading parameter 'resmethod' from configuration file
void prior_from_set (const Setting &set)
 load prior information from set and set internal structures accordingly
void validate ()
 This method TODO.
void resample (ivec &ind)
 resample posterior density (from outside - see MPF)
Array< vec > & __samples ()
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.
Constructors



 PF ()
void set_parameters (int n0, double res_th0=0.5, RESAMPLING_METHOD rm=SYSTEMATIC)
void set_model (shared_ptr< mpdf > par0, shared_ptr< mpdf > obs0)
void set_statistics (const vec w0, const epdf &epdf0)
void set_statistics (const eEmp &epdf0)
Constructors



virtual BM * _copy_ () const
 Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:.
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 epdf * epredictor () const
 Constructs a predictive density $ f(d_{t+1} |d_{t}, \ldots d_{0}) $.
virtual mpdf * predictor () 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

int n
 number of particles;
eEmp est
 posterior density
vec & _w
 pointer into eEmp
Array< vec > & _samples
 pointer into eEmp
shared_ptr< mpdf > par
 Parameter evolution model.
shared_ptr< mpdf > obs
 Observation model.
vec lls
 internal structure storing loglikelihood of predictions
RESAMPLING_METHOD resmethod
 which resampling method will be used
double res_threshold
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)
 Save results to the given logger, details of what is stored is configured by LIDs and options.
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 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::ARXfrg, bdm::KalmanFull, bdm::KalmanCh, 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.

References _samples, bayes_gensmp(), bayes_weights(), do_resampling(), est, lls, n, obs, bdm::eEmp::resample(), and resmethod.

void bdm::PF::from_setting ( const Setting &  set  )  [inline, virtual]

configuration structure for basic PF 

        parameter_pdf   = mpdf_class;         // parameter evolution pdf
        observation_pdf = mpdf_class;         // observation pdf
        prior           = epdf_class;         // prior probability density
        --- optional ---
        n               = 10;                 // number of particles
        resmethod       = 'systematic', or 'multinomial', or 'stratified'
                                                                                  // resampling method
        res_threshold   = 0.5;                // resample when active particles drop below 50%

Reimplemented from bdm::BM.

References bdm::RV::add(), obs, par, prior_from_set(), and resmethod_from_set().

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().

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

Set posterior density by sampling from epdf0 Extends original BM::set_options by two more options:

  • logweights - meaning that all weightes will be logged
  • logsamples - all samples will be also logged

Reimplemented from bdm::BM.

References opt_L_smp, and opt_L_wei.

Member Data Documentation

double bdm::PF::res_threshold [protected]

resampling threshold; in this case its meaning is minimum ratio of active particles For example, for 0.5 resampling is performed when the numebr of active aprticles drops belo 50%.

Referenced by do_resampling(), and resmethod_from_set().

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