bdm::eEmp Class Reference

Weighted empirical density. More...

#include <exp_family.h>

List of all members.

Public Member Functions
void	set_statistics (const vec &w0, const epdf &pdf0)
	Set samples and weights.
void	set_statistics (const epdf &pdf0, int n)
	Set samples and weights.
void	set_samples (const epdf *pdf0)
	Set sample.
void	set_parameters (int n0, bool copy=true)
	Set sample.
void	set_parameters (const Array< vec > &Av)
	Set samples.
vec &	_w ()
	Potentially dangerous, use with care.
const vec &	_w () const
	Potentially dangerous, use with care.
Array< vec > &	_samples ()
	access function
const vec &	_sample (int i) const
	access function
const Array< vec > &	_samples () const
	access function
void	resample (ivec &index, RESAMPLING_METHOD method=SYSTEMATIC)
void	resample (RESAMPLING_METHOD method=SYSTEMATIC)
	Resampling without returning index of new particles.
vec	sample () const
	inherited operation : NOT implemented
double	evallog (const vec &val) const
	inherited operation : NOT implemented
vec	mean () const
	return expected value
vec	variance () const
	return expected variance (not covariance!)
void	qbounds (vec &lb, vec &ub, double perc=0.95) const
	For this class, qbounds are minimum and maximum value of the population!
void	from_setting (const Setting &set)
	Load from structure with elements:.
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.
virtual void	validate ()
	This method TODO.
Constructors
	eEmp ()
	eEmp (const eEmp &e)
	copy constructor
Constructors
Construction of each epdf should support two types of constructors: empty constructor, copy constructor, The following constructors should be supported for convenience: constructor followed by calling set_parameters() constructor accepting random variables calling set_rv() All internal data structures are constructed as empty. Their values (including sizes) will be set by method set_parameters(). This way references can be initialized in constructors.
void	set_parameters (int dim0)
Matematical Operations
virtual mat	sample_m (int N) const
	Returns N samples, from density .
virtual vec	evallog_m (const mat &Val) const
	Compute log-probability of multiple values argument val.
virtual vec	evallog_m (const Array< vec > &Avec) const
	Compute log-probability of multiple values argument val.
virtual shared_ptr< mpdf >	condition (const RV &rv) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
virtual shared_ptr< epdf >	marginal (const RV &rv) const
	Return marginal density on the given RV, the remainig rvs are intergrated out.
Connection to other classes
Description of the random quantity via attribute rv is optional. For operations such as sampling rv does not need to be set. However, for marginalization and conditioning rv has to be set. NB:
void	set_rv (const RV &rv0)
	Name its rv.
bool	isnamed () const
	True if rv is assigned.
const RV &	_rv () const
	Return name (fails when isnamed is false).
Access to attributes
int	dimension () const
	Size of the random variable.
Protected Attributes
int	n
	Number of particles.
vec	w
	Sample weights .
Array< vec >	samples
	Samples .
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.

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Detailed Description

Weighted empirical density.

Used e.g. in particle filters.

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Member Function Documentation

void bdm::epdf::from_setting

(

const Setting &

set

)

[inline, virtual, inherited]

Load from structure with elements:.

         { rv = {class="RV", names=(...),}; // RV describing meaning of random variable
           // elements of offsprings
         }

Reimplemented from bdm::root.

Reimplemented in bdm::enorm< sq_T >, bdm::egiw, bdm::eDirich, bdm::egamma, bdm::euni, bdm::merger_base, bdm::merger_mix, bdm::enorm< ldmat >, bdm::enorm< chmat >, and bdm::enorm< fsqmat >.

References bdm::epdf::set_rv().

void bdm::eEmp::resample	(	ivec &	index,
		RESAMPLING_METHOD	method = SYSTEMATIC
	)

Function performs resampling, i.e. removal of low-weight samples and duplication of high-weight samples such that the new samples represent the same density. The vector with indeces of new samples is returned in variable index.

References bdm_error, n, samples, and w.

Referenced by bdm::PF::bayes(), and bdm::PF::resample().

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

• exp_family.h
• exp_family.cpp