| AnalyticConditionalGaussian | Abstract Class representing all _FULL_ Analytical Conditional gaussians |
| AnalyticConditionalGaussianAdditiveNoise | Abstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise |
| AnalyticMeasurementModelGaussianUncertainty | |
| AnalyticSystemModelGaussianUncertainty | Class for analytic system models with additive Gauss. uncertainty |
| ASIRFilter | ASIR: Auxiliary Particle Filter |
| BackwardFilter | Virtual Baseclass representing all bayesian backward filters |
| BootstrapFilter | Particular particle filter : Proposal PDF = SystemPDF |
| ColumnVector | Wrapper class for ColumnVectors (Boost implementation) |
| ColumnVector_Wrapper | Class ColumnVectorWrapper |
| ConditionalGaussian | Abstract Class representing all Conditional gaussians |
| ConditionalGaussianAdditiveNoise | Abstract Class representing all Conditional Gaussians with additive gaussian noise |
| ConditionalPdf | Abstract Class representing conditional Pdfs P(x | ...) |
| DiscreteConditionalPdf | Abstract Class representing all _FULLY_ Discrete Conditional PDF's |
| DiscretePdf | Class representing a PDF on a discrete variable |
| DiscreteSystemModel | Class for discrete System Models |
| EKFProposalDensity | Proposal Density for non-linear systems with additive Gaussian Noise (using a EKF Filter) |
| EKParticleFilter | Particle filter using EKF for proposal step |
| ExtendedKalmanFilter | |
| Filter | Abstract class representing an interface for Bayesian Filters |
| FilterProposalDensity | Proposal Density for non-linear systems with additive Gaussian Noise (using a (analytic) Filter) |
| Gaussian | Class representing Gaussian (or normal density) |
| HistogramFilter | Class representing the histogram filter |
| InnovationCheck | Class implementing an innovationCheck used in IEKF |
| IteratedExtendedKalmanFilter | |
| KalmanFilter | Class representing the family of all Kalman Filters (EKF, IEKF, ...) |
| LinearAnalyticConditionalGaussian | Linear Conditional Gaussian |
| LinearAnalyticMeasurementModelGaussianUncertainty | Class for linear analytic measurementmodels with additive gaussian noise |
| LinearAnalyticMeasurementModelGaussianUncertainty_Implicit | Class for linear analytic measurementmodels with additive gaussian noise |
| LinearAnalyticSystemModelGaussianUncertainty | Class for linear analytic systemmodels with additive gaussian noise |
| Matrix | Implementation of Matrixwrapper using Boost |
| Matrix_Wrapper | Class Matrixwrapper |
| MCPdf | Monte Carlo Pdf: Sample based implementation of Pdf |
| MeasurementModel | |
| Mixture | Class representing a mixture of PDFs, the mixture can contain different |
| NonLinearAnalyticConditionalGaussian_Ginac | Conditional Gaussian for an analytic nonlinear system using Ginac: |
| NonLinearAnalyticMeasurementModelGaussianUncertainty_Ginac | Class for nonlinear analytic measurementmodels with additive gaussian noise |
| NonLinearAnalyticSystemModelGaussianUncertainty_Ginac | Class for nonlinear analytic systemmodels with additive gaussian noise |
| NonminimalKalmanFilter | |
| OptimalImportanceDensity | Optimal importance density for Nonlinear Gaussian SS Models |
| Optimalimportancefilter | Particular particle filter: Proposal PDF = Optimal Importance function |
| ParticleFilter | Virtual Class representing all particle filters |
| ParticleSmoother | Class representing a particle backward filter |
| Class PDF: Virtual Base class representing Probability Density Functions | |
| Probability | Class representing a probability (a double between 0 and 1) |
| RauchTungStriebel | Class representing all Rauch-Tung-Striebel backward filters |
| RowVector | Wrapper class for RowVectors (Boost implementation) |
| RowVector_Wrapper | Class RowVectorWrapper |
| Sample | |
| SRIteratedExtendedKalmanFilter | |
| SymmetricMatrix_Wrapper | Class SymmetricMatrixWrapper |
| SystemModel | |
| Uniform | Class representing uniform density |
| WeightedSample |
1.5.5