# PF Class Reference

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

`#include <libPF.h>`

Inheritance diagram for PF:

[legend]
Collaboration diagram for PF:

[legend]

List of all members.

## Public Member Functions
| PF (const RV &rv0, mpdf &par0, mpdf &obs0, int n0)
| Default constructor.
void | set_est (const epdf &epdf0)
| Set posterior density by sampling from epdf0.
void | bayes (const vec &dt)
| Incremental Bayes rule.
void | bayes (mat Dt)
| Batch Bayes rule (columns of Dt are observations).
virtual epdf & | _epdf ()=0
| Returns a pointer to the epdf representing posterior density on parameters. Use with care!
const RV & | _rv () const
| access function
double | _ll () const
| access function
## 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.
RV | rv
| Random variable of the posterior.
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 time.

## 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

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

Incremental Bayes rule.

Parameters:

  | dt | vector of input data

Implements BM.

Reimplemented in MPF< BM_T >.

References _samples, _w, est, mpdf::evalcond(), n, obs, par, eEmp::resample(), and mpdf::samplecond().

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

- work/mixpp/bdm/estim/libPF.h
- work/mixpp/bdm/estim/libPF.cpp