Bayesian Filtering Library Generated from SVN r
Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123456]
 CBackwardFilter< StateVar >Virtual Baseclass representing all bayesian backward filters
 CParticleSmoother< StateVar >Class representing a particle backward filter
 CBackwardFilter< MatrixWrapper::ColumnVector >
 CRauchTungStriebelClass representing all Rauch-Tung-Striebel backward filters
 CColumnVector_WrapperClass ColumnVectorWrapper
 CFilter< StateVar, MeasVar >Abstract class representing an interface for Bayesian Filters
 CParticleFilter< ColumnVector, ColumnVector >
 CEKParticleFilterParticle filter using EKF for proposal step
 CMixtureParticleFilter< StateVar, MeasVar >Virtual Class representing all Mixture particle filters
 CMixtureBootstrapFilter< StateVar, MeasVar >Particular mixture particle filter : Proposal PDF = SystemPDF
 CParticleFilter< StateVar, MeasVar >Virtual Class representing all particle filters
 CASIRFilter< StateVar, MeasVar >ASIR: Auxiliary Particle Filter
 CBootstrapFilter< StateVar, MeasVar >Particular particle filter : Proposal PDF = SystemPDF
 COptimalimportancefilter< StateVar, MeasVar >Particular particle filter: Proposal PDF = Optimal Importance function
 CFilter< ColumnVector, ColumnVector >
 CFilter< int, MeasVar >
 CHistogramFilter< MeasVar >Class representing the histogram filter
 CFilter< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >
 CKalmanFilterClass representing the family of all Kalman Filters (EKF, IEKF, ...)
 CExtendedKalmanFilter
 CIteratedExtendedKalmanFilter
 CNonminimalKalmanFilter
 CSRIteratedExtendedKalmanFilter
 CInnovationCheckClass implementing an innovationCheck used in IEKF
 CMatrix_WrapperClass Matrixwrapper
 CMeasurementModel< MeasVar, StateVar >
 CMeasurementModel< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >
 CAnalyticMeasurementModelGaussianUncertainty
 CLinearAnalyticMeasurementModelGaussianUncertaintyClass for linear analytic measurementmodels with additive gaussian noise
 CLinearAnalyticMeasurementModelGaussianUncertainty_ImplicitClass for linear analytic measurementmodels with additive gaussian noise
 CNonLinearAnalyticMeasurementModelGaussianUncertainty_GinacClass for nonlinear analytic measurementmodels with additive gaussian noise
 CPdf< T >Class PDF: Virtual Base class representing Probability Density Functions
 CConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >
 CConditionalGaussianAbstract Class representing all Conditional gaussians
 CAnalyticConditionalGaussianAbstract Class representing all FULL Analytical Conditional gaussians
 CAnalyticConditionalGaussianAdditiveNoiseAbstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise
 CLinearAnalyticConditionalGaussianLinear Conditional Gaussian
 CNonLinearAnalyticConditionalGaussian_GinacConditional Gaussian for an analytic nonlinear system using Ginac:
 CFilterProposalDensityProposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter)
 CEKFProposalDensityProposal Density for non-linear systems with additive Gaussian Noise (using a EKF Filter)
 COptimalImportanceDensityOptimal importance density for Nonlinear Gaussian SS Models
 CConditionalGaussianAdditiveNoiseAbstract Class representing all Conditional Gaussians with additive gaussian noise
 CConditionalPdf< int, int >
 CDiscreteConditionalPdfClass representing all FULLY Discrete Conditional PDF's
 CConditionalPdf< ColumnVector, ColumnVector >
 CConditionalPdf< MeasVar, StateVar >
 CConditionalPdf< StateVar, StateVar >
 CConditionalPdf< T, T >
 CMCPdf< T >Monte Carlo Pdf: Sample based implementation of Pdf
 CMixture< T >Class representing a mixture of PDFs, the mixture can contain different
 CPdf< ColumnVector >
 CPdf< int >
 CDiscretePdfClass representing a PDF on a discrete variable
 CPdf< MatrixWrapper::ColumnVector >
 CGaussianClass representing Gaussian (or normal density)
 CUniformClass representing uniform density
 CPdf< MeasVar >
 CPdf< StateVar >
 CPdf< Var >
 CConditionalPdf< Var, CondArg >Abstract Class representing conditional Pdfs P(x | ...)
 CProbabilityClass representing a probability (a double between 0 and 1)
 CRowVector_WrapperClass RowVectorWrapper
 CSample< T >
 CWeightedSample< ColumnVector >
 CWeightedSample< StateVar >
 CWeightedSample< T >
 CSample< ColumnVector >
 CSample< StateVar >
 CSymmetricMatrix_WrapperClass SymmetricMatrixWrapper
 CSystemModel< T >
 CSystemModel< int >
 CDiscreteSystemModelClass for discrete System Models
 CSystemModel< MatrixWrapper::ColumnVector >
 CAnalyticSystemModelGaussianUncertaintyClass for analytic system models with additive Gauss. uncertainty
 CLinearAnalyticSystemModelGaussianUncertaintyClass for linear analytic systemmodels with additive gaussian noise
 CNonLinearAnalyticSystemModelGaussianUncertainty_GinacClass for nonlinear analytic systemmodels with additive gaussian noise