Bayesian Filtering Library Generated from SVN r
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 12]
 NBFL
 CAnalyticConditionalGaussianAbstract Class representing all FULL Analytical Conditional gaussians
 CAnalyticConditionalGaussianAdditiveNoiseAbstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise
 CAnalyticMeasurementModelGaussianUncertainty
 CAnalyticSystemModelGaussianUncertaintyClass for analytic system models with additive Gauss. uncertainty
 CASIRFilterASIR: Auxiliary Particle Filter
 CBackwardFilterVirtual Baseclass representing all bayesian backward filters
 CBootstrapFilterParticular particle filter : Proposal PDF = SystemPDF
 CConditionalGaussianAbstract Class representing all Conditional gaussians
 CConditionalGaussianAdditiveNoiseAbstract Class representing all Conditional Gaussians with additive gaussian noise
 CConditionalPdfAbstract Class representing conditional Pdfs P(x | ...)
 CDiscreteConditionalPdfClass representing all FULLY Discrete Conditional PDF's
 CDiscretePdfClass representing a PDF on a discrete variable
 CDiscreteSystemModelClass for discrete System Models
 CEKFProposalDensityProposal Density for non-linear systems with additive Gaussian Noise (using a EKF Filter)
 CEKParticleFilterParticle filter using EKF for proposal step
 CExtendedKalmanFilter
 CFilterAbstract class representing an interface for Bayesian Filters
 CFilterProposalDensityProposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter)
 CGaussianClass representing Gaussian (or normal density)
 CHistogramFilterClass representing the histogram filter
 CInnovationCheckClass implementing an innovationCheck used in IEKF
 CIteratedExtendedKalmanFilter
 CKalmanFilterClass representing the family of all Kalman Filters (EKF, IEKF, ...)
 CLinearAnalyticConditionalGaussianLinear Conditional Gaussian
 CLinearAnalyticMeasurementModelGaussianUncertaintyClass for linear analytic measurementmodels with additive gaussian noise
 CLinearAnalyticMeasurementModelGaussianUncertainty_ImplicitClass for linear analytic measurementmodels with additive gaussian noise
 CLinearAnalyticSystemModelGaussianUncertaintyClass for linear analytic systemmodels with additive gaussian noise
 CMCPdfMonte Carlo Pdf: Sample based implementation of Pdf
 CMeasurementModel
 CMixtureClass representing a mixture of PDFs, the mixture can contain different
 CMixtureBootstrapFilterParticular mixture particle filter : Proposal PDF = SystemPDF
 CMixtureParticleFilterVirtual Class representing all Mixture particle filters
 CNonLinearAnalyticConditionalGaussian_GinacConditional Gaussian for an analytic nonlinear system using Ginac:
 CNonLinearAnalyticMeasurementModelGaussianUncertainty_GinacClass for nonlinear analytic measurementmodels with additive gaussian noise
 CNonLinearAnalyticSystemModelGaussianUncertainty_GinacClass for nonlinear analytic systemmodels with additive gaussian noise
 CNonminimalKalmanFilter
 COptimalImportanceDensityOptimal importance density for Nonlinear Gaussian SS Models
 COptimalimportancefilterParticular particle filter: Proposal PDF = Optimal Importance function
 CParticleFilterVirtual Class representing all particle filters
 CParticleSmootherClass representing a particle backward filter
 CPdfClass PDF: Virtual Base class representing Probability Density Functions
 CProbabilityClass representing a probability (a double between 0 and 1)
 CRauchTungStriebelClass representing all Rauch-Tung-Striebel backward filters
 CSample
 CSRIteratedExtendedKalmanFilter
 CSystemModel
 CUniformClass representing uniform density
 CWeightedSample
 NMatrixWrapper
 CColumnVector_WrapperClass ColumnVectorWrapper
 CMatrix_WrapperClass Matrixwrapper
 CRowVector_WrapperClass RowVectorWrapper
 CSymmetricMatrix_WrapperClass SymmetricMatrixWrapper