mrpt::bayes::CRejectionSamplingCapable Class Reference

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Detailed Description

A base class for implementing rejection sampling in a generic state space.

See the main method CRejectionSamplingCapable::rejectionSampling To use this class, create your own class as a child of this one and implement the desired virtual methods, and add any required internal data.

#include <mrpt/bayes/CRejectionSamplingCapable.h>

Inheritance diagram for mrpt::bayes::CRejectionSamplingCapable:

List of all members.

Public Types
typedef CProbabilityParticle < TStateSpace >	TParticle
Public Member Functions
virtual	~CRejectionSamplingCapable ()
	Virtual destructor.
void	rejectionSampling (size_t desiredSamples, std::vector< TParticle > &outSamples, size_t timeoutTrials=1000)
	Generates a set of N independent samples via rejection sampling.
Protected Member Functions
virtual void	RS_drawFromProposal (TStateSpace &outSample)=0
	Generates one sample, drawing from some proposal distribution.
virtual double	RS_observationLikelihood (const TStateSpace &x)=0
	Returns the NORMALIZED observation likelihood (linear, not exponential!!!) at a given point of the state space (values in the range [0,1]).

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Member Typedef Documentation

typedef CProbabilityParticle<TStateSpace> mrpt::bayes::CRejectionSamplingCapable::TParticle

Definition at line 50 of file CRejectionSamplingCapable.h.

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Constructor & Destructor Documentation

virtual mrpt::bayes::CRejectionSamplingCapable::~CRejectionSamplingCapable

(

)

[inline, virtual]

Virtual destructor.

Definition at line 54 of file CRejectionSamplingCapable.h.

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Member Function Documentation

void mrpt::bayes::CRejectionSamplingCapable::rejectionSampling	(	size_t	desiredSamples,
		std::vector< TParticle > &	outSamples,
		size_t	timeoutTrials = 1000
	)		[inline]

Generates a set of N independent samples via rejection sampling.

Parameters:

desiredSamples	The number of desired samples to generate
outSamples	The output samples.
timeoutTrials	The maximum number of rejection trials for each generated sample (i.e. the maximum number of iterations). This can be used to set a limit to the time complexity of the algorithm for difficult probability densities. All will have equal importance weights (a property of rejection sampling), although those samples generated at timeout will have a different importance weights.

Definition at line 65 of file CRejectionSamplingCapable.h.

virtual void mrpt::bayes::CRejectionSamplingCapable::RS_drawFromProposal

(

TStateSpace &

outSample

)

[protected, pure virtual]

Generates one sample, drawing from some proposal distribution.

Referenced by mrpt::bayes::CRejectionSamplingCapable< poses::CPose2D >::rejectionSampling().

virtual double mrpt::bayes::CRejectionSamplingCapable::RS_observationLikelihood

(

const TStateSpace &

x

)

[protected, pure virtual]

Returns the NORMALIZED observation likelihood (linear, not exponential!!!) at a given point of the state space (values in the range [0,1]).

Referenced by mrpt::bayes::CRejectionSamplingCapable< poses::CPose2D >::rejectionSampling().