Optimal importance density for Nonlinear Gaussian SS Models. More...

#include <optimal_importance_density.h>

Inheritance diagram for BFL::OptimalImportanceDensity:

[legend]

Public Member Functions
virtual SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...

virtual Matrix	dfGet (int i) const

virtual ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf. More...

	OptimalImportanceDensity (AnalyticConditionalGaussian SystemPdf, LinearAnalyticConditionalGaussian MeasPdf)
	Constructor. More...

virtual	~OptimalImportanceDensity ()
	Destructor. More...

Public Member Functions inherited from BFL::AnalyticConditionalGaussian
	AnalyticConditionalGaussian (int dim=0, int num_conditional_arguments=0)
	Constructor. More...

virtual MatrixWrapper::Matrix	dfGet (unsigned int i) const
	returns derivative from function to n-th conditional variable More...

virtual	~AnalyticConditionalGaussian ()
	Destructor. More...

Public Member Functions inherited from BFL::ConditionalGaussian
virtual ConditionalGaussian *	Clone () const
	Clone function. More...

	ConditionalGaussian (int dim=0, int num_conditional_arguments=0)
	Constructor. More...

virtual Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
	Get the probability of a certain argument. More...

virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, int method=DEFAULT, void *args=NULL) const

virtual bool	SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const int num_samples, int method=DEFAULT, void *args=NULL) const

virtual	~ConditionalGaussian ()
	Destructor. More...

Public Member Functions inherited from BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >
const MatrixWrapper::ColumnVector &	ConditionalArgumentGet (unsigned int n_argument) const
	Get the n-th argument of the list. More...

virtual void	ConditionalArgumentSet (unsigned int n_argument, const MatrixWrapper::ColumnVector &argument)
	Set the n-th argument of the list. More...

const std::vector< MatrixWrapper::ColumnVector > &	ConditionalArgumentsGet () const
	Get the whole list of conditional arguments. More...

virtual void	ConditionalArgumentsSet (std::vector< MatrixWrapper::ColumnVector > ConditionalArguments)
	Set the whole list of conditional arguments. More...

	ConditionalPdf (int dimension=0, unsigned int num_conditional_arguments=0)
	Constructor. More...

unsigned int	NumConditionalArgumentsGet () const
	Get the Number of conditional arguments. More...

virtual void	NumConditionalArgumentsSet (unsigned int numconditionalarguments)
	Set the Number of conditional arguments. More...

virtual	~ConditionalPdf ()
	Destructor. More...

Public Member Functions inherited from BFL::BFL::Pdf< MatrixWrapper::ColumnVector >
unsigned int	DimensionGet () const
	Get the dimension of the argument. More...

virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument. More...

	Pdf (unsigned int dimension=0)
	Constructor. More...

virtual bool	SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const
	Draw multiple samples from the Pdf (overloaded) More...

virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf: More...

virtual	~Pdf ()
	Destructor. More...

Private Attributes
LinearAnalyticConditionalGaussian *	_MeasPdf

AnalyticConditionalGaussian *	_SystemPdf

Additional Inherited Members
Protected Attributes inherited from BFL::ConditionalGaussian
ColumnVector	_diff

Matrix	_Low_triangle

ColumnVector	_Mu

ColumnVector	_samples

ColumnVector	_SampleValue

Detailed Description

Optimal importance density for Nonlinear Gaussian SS Models.

Describes the optimal importance density for all systems of the form

$x_k = f(x_{k-1}) + v_k, \quad v_k \sim N(0, \Sigma_v)$

$z_k = H x_k + w_k, \quad w_k \sim N(0, \Sigma_w)$

This means all systems with a system equation that uses a AnalyticConditionalGaussian Class and a measurement equation that uses a LinearAnalyticConditionalGaussian class

Definition at line 37 of file optimal_importance_density.h.

Constructor & Destructor Documentation

◆ OptimalImportanceDensity()

BFL::OptimalImportanceDensity::OptimalImportanceDensity	(	AnalyticConditionalGaussian *	SystemPdf,
		LinearAnalyticConditionalGaussian *	MeasPdf
	)

Constructor.

Parameters

SystemPdf
MeasPdf

◆ ~OptimalImportanceDensity()

virtual BFL::OptimalImportanceDensity::~OptimalImportanceDensity ( )

virtual

Destructor.

Member Function Documentation

◆ CovarianceGet()

virtual SymmetricMatrix BFL::OptimalImportanceDensity::CovarianceGet ( ) const

virtual

Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.

Get first order statistic (Covariance) of this AnalyticPdf

Returns: The Covariance of the Pdf (a SymmetricMatrix of dim DIMENSION)

Todo:: extend this more general to n-th order statistic

Bug:: Discrete pdfs should not be able to use this!

Reimplemented from BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

◆ dfGet()

virtual Matrix BFL::OptimalImportanceDensity::dfGet ( int i ) const

virtual

◆ ExpectedValueGet()

virtual ColumnVector BFL::OptimalImportanceDensity::ExpectedValueGet ( ) const

virtual

Get the expected value E[x] of the pdf.

Get low order statistic (Expected Value) of this AnalyticPdf

Returns: The Expected Value of the Pdf (a ColumnVector with DIMENSION rows)

Note: No set functions here! This can be useful for analytic functions, but not for sample based representations!; For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?

Reimplemented from BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.