Bayesian Filtering Library  Generated from SVN r
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
AnalyticConditionalGaussianAbstract Class representing all _FULL_ Analytical Conditional gaussians
AnalyticConditionalGaussianAdditiveNoiseAbstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise
AnalyticMeasurementModelGaussianUncertainty
AnalyticSystemModelGaussianUncertaintyClass for analytic system models with additive Gauss. uncertainty
ASIRFilter< StateVar, MeasVar >ASIR: Auxiliary Particle Filter
BackwardFilter< StateVar >Virtual Baseclass representing all bayesian backward filters
BootstrapFilter< StateVar, MeasVar >Particular particle filter : Proposal PDF = SystemPDF
ColumnVectorWrapper class for ColumnVectors (Boost implementation)
ColumnVector_WrapperClass ColumnVectorWrapper
ColumnVector_WrapperClass ColumnVectorWrapper
ConditionalGaussianAbstract Class representing all Conditional gaussians
ConditionalGaussianAdditiveNoiseAbstract Class representing all Conditional Gaussians with additive gaussian noise
ConditionalPdf< Var, CondArg >Abstract Class representing conditional Pdfs P(x | ...)
DiscreteConditionalPdfAbstract Class representing all _FULLY_ Discrete Conditional PDF's
DiscretePdfClass representing a PDF on a discrete variable
DiscretePdfClass representing a PDF on a discrete variable
DiscreteSystemModelClass for discrete System Models
EKFProposalDensityProposal Density for non-linear systems with additive Gaussian Noise (using a EKF Filter)
EKParticleFilterParticle filter using EKF for proposal step
ExtendedKalmanFilter
Filter< StateVar, MeasVar >Abstract class representing an interface for Bayesian Filters
FilterProposalDensityProposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter)
GaussianClass representing Gaussian (or normal density)
HistogramFilter< MeasVar >Class representing the histogram filter
InnovationCheckClass implementing an innovationCheck used in IEKF
IteratedExtendedKalmanFilter
KalmanFilterClass representing the family of all Kalman Filters (EKF, IEKF, ...)
LinearAnalyticConditionalGaussianLinear Conditional Gaussian
LinearAnalyticMeasurementModelGaussianUncertaintyClass for linear analytic measurementmodels with additive gaussian noise
LinearAnalyticMeasurementModelGaussianUncertainty_ImplicitClass for linear analytic measurementmodels with additive gaussian noise
LinearAnalyticSystemModelGaussianUncertaintyClass for linear analytic systemmodels with additive gaussian noise
MatrixImplementation of Matrixwrapper using Boost
Matrix_WrapperClass Matrixwrapper
Matrix_WrapperClass Matrixwrapper
MCPdf< T >Monte Carlo Pdf: Sample based implementation of Pdf
MCPdf< T >Monte Carlo Pdf: Sample based implementation of Pdf
MeasurementModel< MeasVar, StateVar >
Mixture< T >Class representing a mixture of PDFs, the mixture can contain different
Mixture< T >Class representing a mixture of PDFs, the mixture can contain different
MixtureBootstrapFilter< StateVar, MeasVar >Particular mixture particle filter : Proposal PDF = SystemPDF
MixtureParticleFilter< StateVar, MeasVar >Virtual Class representing all Mixture particle filters
NonLinearAnalyticConditionalGaussian_GinacConditional Gaussian for an analytic nonlinear system using Ginac:
NonLinearAnalyticMeasurementModelGaussianUncertainty_GinacClass for nonlinear analytic measurementmodels with additive gaussian noise
NonLinearAnalyticSystemModelGaussianUncertainty_GinacClass for nonlinear analytic systemmodels with additive gaussian noise
NonminimalKalmanFilter
OptimalImportanceDensityOptimal importance density for Nonlinear Gaussian SS Models
Optimalimportancefilter< StateVar, MeasVar >Particular particle filter: Proposal PDF = Optimal Importance function
ParticleFilter< StateVar, MeasVar >Virtual Class representing all particle filters
ParticleSmoother< StateVar >Class representing a particle backward filter
Pdf< T >Class PDF: Virtual Base class representing Probability Density Functions
Pdf< T >Class PDF: Virtual Base class representing Probability Density Functions
ProbabilityClass representing a probability (a double between 0 and 1)
ProbabilityClass representing a probability (a double between 0 and 1)
RauchTungStriebelClass representing all Rauch-Tung-Striebel backward filters
RowVectorWrapper class for RowVectors (Boost implementation)
RowVector_WrapperClass RowVectorWrapper
RowVector_WrapperClass RowVectorWrapper
Sample< T >
Sample< T >
SRIteratedExtendedKalmanFilter
SymmetricMatrix
SymmetricMatrix_WrapperClass SymmetricMatrixWrapper
SymmetricMatrix_WrapperClass SymmetricMatrixWrapper
SystemModel< T >
UniformClass representing uniform density
WeightedSample< T >
WeightedSample< T >