# bdm::MPF< BM_T > Class Template Reference

Marginalized Particle filter. More...

`#include <libPF.h>`

List of all members.

## Classes
class | mpfepdf
| internal class for MPDF providing composition of eEmp with external components
## Public Member Functions
| MPF ()
| Default constructor.
void | set_parameters (mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC)
void | set_statistics (epdf *epdf0, const BM_T *BMcond0)
void | bayes (const vec &dt)
| Incremental Bayes rule.
const epdf & | posterior () const
const epdf * | _e () const
void | set_options (const string &opt)
| Set postrior of `rvc` to samples from epdf0. Statistics of BMs are not re-computed! Use only for initialization!
BM * | _BM (int i)
| Access function.
vec * | __w ()
| access function
Constructors
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 epdf * | epredictor () const
| Constructs a predictive density .
virtual mpdf * | predictor () 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
int | n
| number of particles;
eEmp | est
| posterior density
vec & | _w
| pointer into `eEmp`
Array< vec > & | _samples
| pointer into `eEmp`
mpdf * | par
| Parameter evolution model.
mpdf * | obs
| 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
void | log_add (logger *L, const string &name="")
| Add all logged variables to a logger.
void | logit (logger *L)
ivec | LIDs
| IDs of storages in loggers.
bool | opt_L_bounds
| Option for logging bounds.

## Detailed Description

### template<class BM_T>
class bdm::MPF< BM_T >

Marginalized Particle filter.

Trivial version: proposal = parameter evolution, observation model is not used. (it is assumed to be part of BM).

## 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.

template<class BM_T >

void bdm::MPF< BM_T >::bayes | ( | const vec & | dt | ) | `[inline, virtual]`

Incremental Bayes rule.

Parameters:

  | dt | vector of input data

Reimplemented from bdm::PF.

References bdm::mpdf::_e(), bdm::PF::_samples, bdm::PF::_w, bdm::PF::est, bdm::epdf::evallog(), bdm::PF::par, bdm::eEmp::resample(), bdm::PF::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().

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

- libPF.h