bdm::eEmp Class Reference

#include <libEF.h>

Inheritance diagram for bdm::eEmp:

Detailed Description

Weighted empirical density.

Used e.g. in particle filters.

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.

vec & _w ()

Potentially dangerous, use with care.

const vec & _w () const

Potentially dangerous, use with care.

Array< vec > & _samples ()

access function

const Array< vec > & _samples () const

access function

ivec resample (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.

vec sample () const

inherited operation : NOT implemneted

double evallog (const vec &val) const

inherited operation : NOT implemneted

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!

Constructors

eEmp ()

eEmp (const eEmp &e)

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, $[x_1 , x_2 , \ldots \$ from density $ f_x(rv)$ .

virtual vec evallog_m (const mat &Val) const

Compute log-probability of multiple values argument val.

virtual mpdf * condition (const RV &rv) const

Return conditional density on the given RV, the remaining rvs will be in conditioning.

virtual 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 $w$ .

Array< vec > samples

Samples $x^{(i)}, i=1..n$ .

int dim

dimension of the random variable

RV rv

Description of the random variable.

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

libEF.h
libEF.cpp


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.
vec &	_w ()
	Potentially dangerous, use with care.
const vec &	_w () const
	Potentially dangerous, use with care.
Array< vec > &	_samples ()
	access function
const Array< vec > &	_samples () const
	access function
ivec	resample (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.
vec	sample () const
	inherited operation : NOT implemneted
double	evallog (const vec &val) const
	inherited operation : NOT implemneted
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!
Constructors
	eEmp ()
	eEmp (const eEmp &e)
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, $[x_1 , x_2 , \ldots \$ from density .
virtual vec	evallog_m (const mat &Val) const
	Compute log-probability of multiple values argument `val`.
virtual mpdf *	condition (const RV &rv) const
	Return conditional density on the given RV, the remaining rvs will be in conditioning.
virtual 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 $x^{(i)}, i=1..n$ .
int	dim
	dimension of the random variable
RV	rv
	Description of the random variable.