# bdm::MPF Class Reference

Marginalized Particle filter. More...

## Detailed Description

Marginalized Particle filter.

A composition of particle filter with exact (or almost exact) bayesian models (BMs). The Bayesian models provide marginalized predictive density. Internaly this is achieved by virtual class MPFmpdf.

`#include <particles.h>`

List of all members.

## Classes
class | mpfepdf
| internal class for MPDF providing composition of eEmp with external components More...
## Public Member Functions
| MPF ()
| Default constructor.
void | set_parameters (shared_ptr< mpdf > par0, shared_ptr< mpdf > obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC)
| set all parameters at once
void | set_BM (const BM &BMcond0)
| set a prototype of BM, copy it to as many times as there is particles in pf
void | bayes (const vec &dt)
| Incremental Bayes rule.
const epdf & | posterior () const
| return posterior density
void | set_options (const string &opt)
const BM * | _BM (int i)
| Access function.
void | from_setting (const Setting &set)
void | validate ()
| This method TODO.
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
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 .
virtual mpdf * | predictor () const
| Constructs conditional density of 1-step ahead predictor .
Access to attributes
access function
const RV & | _drv () const
void | set_drv (const RV &rv)
| access function
void | set_rv (const RV &rv)
| access to rv of the posterior
double | _ll () const
| return internal log-likelihood of the last data vector
void | set_evalll (bool evl0)
| switch evaluation of log-likelihood on/off
## Protected Attributes
shared_ptr< PF > | pf
| particle filter on non-linear variable
Array< BM * > | BMs
| Array of Bayesian models.
mpfepdf | jest
| Density joining PF.est with conditional parts.
bool | opt_L_mea
| Log means of BMs.
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
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.

Referenced by set_BM().

void bdm::MPF::bayes | ( | const vec & | dt | ) | `[virtual]`

Incremental Bayes rule.

Parameters:

  | dt | vector of input data

Implements bdm::BM.

References BMs, bdm::BM::condition(), and pf.

void bdm::MPF::from_setting | ( | const Setting & | set | ) | `[inline, virtual]`

configuration structure for basic PF

            BM              = BM_class;           // Bayesian filtr for analytical part of the model
            parameter_pdf   = mpdf_class;         // transitional pdf for non-parametric part of the model
            prior           = epdf_class;         // prior probability density
            --- optional ---
            n               = 10;                 // number of particles
            resmethod       = 'systematic', or 'multinomial', or 'stratified'
                                                                                      // resampling method

Reimplemented from bdm::BM.

References bdm::UI::get(), pf, set_BM(), bdm::BM::set_drv(), set_options(), and validate().

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::MPF::set_options | ( | const string & | opt | ) | `[inline, virtual]`

Extends options understood by BM::set_options by option

- logmeans - meaning

Reimplemented from bdm::BM.

References opt_L_mea.

Referenced by from_setting().

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

- particles.h
- particles.cpp