Bayesian Filtering Library Generated from SVN r
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Class PDF: Virtual Base class representing Probability Density Functions. More...
#include <pdf.h>
Public Member Functions | |
Pdf (unsigned int dimension=0) | |
Constructor. | |
virtual | ~Pdf () |
Destructor. | |
virtual Pdf< T > * | Clone () const =0 |
Pure virtual clone function. | |
virtual bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded) | |
virtual bool | SampleFrom (Sample< T > &one_sample, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf: | |
virtual Probability | ProbabilityGet (const T &input) const |
Get the probability of a certain argument. | |
unsigned int | DimensionGet () const |
Get the dimension of the argument. | |
virtual void | DimensionSet (unsigned int dim) |
Set the dimension of the argument. | |
virtual T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
Class PDF: Virtual Base class representing Probability Density Functions.
( | unsigned int | dimension = 0 | ) |
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pure virtual |
Pure virtual clone function.
Implemented in ConditionalGaussian, ConditionalPdf< Var, CondArg >, ConditionalPdf< ColumnVector, ColumnVector >, ConditionalPdf< int, int >, ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, ConditionalPdf< MeasVar, StateVar >, ConditionalPdf< StateVar, StateVar >, ConditionalPdf< T, T >, DiscreteConditionalPdf, DiscretePdf, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, and Uniform.
Referenced by Mixture< T >::AddComponent(), and Mixture< T >::AddComponent().
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virtual |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
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inline |
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virtual |
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Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
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virtual |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, Mixture< T >, and Uniform.
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virtual |
Draw 1 sample from the Pdf:
There's no need to create a list for only 1 sample!
one_sample | sample that will contain result of sampling |
method | Sampling method to be used. Each sampling method is currently represented by an enum, eg. SampleMthd::BOXMULLER |
args | Pointer to a struct representing extra sample arguments |
Reimplemented in ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, MCPdf< T >, Mixture< T >, and Uniform.
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virtual |
Draw multiple samples from the Pdf (overloaded)
list_samples | list of samples that will contain result of sampling |
num_samples | Number of Samples to be drawn (iid) |
method | Sampling method to be used. Each sampling method is currently represented by an enum eg. SampleMthd::BOXMULLER |
args | Pointer to a struct representing extra sample arguments. "Sample Arguments" can be anything (the number of steps a gibbs-iterator should take, the interval width in MCMC, ... (or nothing), so it is hard to give a meaning to what exactly Sample Arguments should represent... |
Reimplemented in DiscreteConditionalPdf, DiscretePdf, Gaussian, MCPdf< T >, Mixture< T >, and Uniform.
Definition at line 179 of file pdf.h.
Referenced by MCPdf< T >::SampleFrom(), and Mixture< T >::SampleFrom().