Optimal importance density for Nonlinear Gaussian SS Models. More...
#include <optimal_importance_density.h>
Inheritance diagram for BFL::OptimalImportanceDensity:
Public Member Functions | |
| virtual SymmetricMatrix | CovarianceGet () const |
| Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More... | |
| virtual Matrix | dfGet (int i) const |
| virtual ColumnVector | ExpectedValueGet () const |
| Get the expected value E[x] of the pdf. More... | |
| OptimalImportanceDensity (AnalyticConditionalGaussian *SystemPdf, LinearAnalyticConditionalGaussian *MeasPdf) | |
| Constructor. More... | |
| virtual | ~OptimalImportanceDensity () |
| Destructor. More... | |
 Public Member Functions inherited from BFL::AnalyticConditionalGaussian | |
| AnalyticConditionalGaussian (int dim=0, int num_conditional_arguments=0) | |
| Constructor. More... | |
| virtual MatrixWrapper::Matrix | dfGet (unsigned int i) const |
| returns derivative from function to n-th conditional variable More... | |
| virtual | ~AnalyticConditionalGaussian () |
| Destructor. More... | |
| Public Member Functions inherited from BFL::ConditionalGaussian | |
| virtual ConditionalGaussian * | Clone () const |
| Clone function. More... | |
| ConditionalGaussian (int dim=0, int num_conditional_arguments=0) | |
| Constructor. More... | |
| virtual Probability | ProbabilityGet (const MatrixWrapper::ColumnVector &input) const |
| Get the probability of a certain argument. More... | |
| virtual bool | SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, int method=DEFAULT, void *args=NULL) const |
| virtual bool | SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const int num_samples, int method=DEFAULT, void *args=NULL) const |
| virtual | ~ConditionalGaussian () |
| Destructor. More... | |
| Public Member Functions inherited from BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > | |
| const MatrixWrapper::ColumnVector & | ConditionalArgumentGet (unsigned int n_argument) const |
| Get the n-th argument of the list. More... | |
| virtual void | ConditionalArgumentSet (unsigned int n_argument, const MatrixWrapper::ColumnVector &argument) |
| Set the n-th argument of the list. More... | |
| const std::vector< MatrixWrapper::ColumnVector > & | ConditionalArgumentsGet () const |
| Get the whole list of conditional arguments. More... | |
| virtual void | ConditionalArgumentsSet (std::vector< MatrixWrapper::ColumnVector > ConditionalArguments) |
| Set the whole list of conditional arguments. More... | |
| ConditionalPdf (int dimension=0, unsigned int num_conditional_arguments=0) | |
| Constructor. More... | |
| unsigned int | NumConditionalArgumentsGet () const |
| Get the Number of conditional arguments. More... | |
| virtual void | NumConditionalArgumentsSet (unsigned int numconditionalarguments) |
| Set the Number of conditional arguments. More... | |
| virtual | ~ConditionalPdf () |
| Destructor. More... | |
| Public Member Functions inherited from BFL::BFL::Pdf< MatrixWrapper::ColumnVector > | |
| unsigned int | DimensionGet () const |
| Get the dimension of the argument. More... | |
| virtual void | DimensionSet (unsigned int dim) |
| Set the dimension of the argument. More... | |
| Pdf (unsigned int dimension=0) | |
| Constructor. More... | |
| virtual bool | SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
| Draw multiple samples from the Pdf (overloaded) More... | |
| virtual bool | SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const |
| Draw 1 sample from the Pdf: More... | |
| virtual | ~Pdf () |
| Destructor. More... | |
Private Attributes | |
| LinearAnalyticConditionalGaussian * | _MeasPdf |
| AnalyticConditionalGaussian * | _SystemPdf |
Additional Inherited Members | |
| Protected Attributes inherited from BFL::ConditionalGaussian | |
| ColumnVector | _diff |
| Matrix | _Low_triangle |
| ColumnVector | _Mu |
| ColumnVector | _samples |
| ColumnVector | _SampleValue |
Detailed Description
Optimal importance density for Nonlinear Gaussian SS Models.
Describes the optimal importance density for all systems of the form
![\[ x_k = f(x_{k-1}) + v_k, \quad v_k \sim N(0, \Sigma_v) \]](form_59.png)
![\[ z_k = H x_k + w_k, \quad w_k \sim N(0, \Sigma_w) \]](form_60.png)
This means all systems with a system equation that uses a AnalyticConditionalGaussian Class and a measurement equation that uses a LinearAnalyticConditionalGaussian class
Definition at line 37 of file optimal_importance_density.h.
Constructor & Destructor Documentation
| BFL::OptimalImportanceDensity::OptimalImportanceDensity | ( | AnalyticConditionalGaussian * | SystemPdf, |
| LinearAnalyticConditionalGaussian * | MeasPdf | ||
| ) |
Constructor.
- Parameters
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SystemPdf MeasPdf
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virtual |
Destructor.
Member Function Documentation
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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
- Returns
- The Covariance of the Pdf (a SymmetricMatrix of dim DIMENSION)
- Todo:
- extend this more general to n-th order statistic
- Bug:
- Discrete pdfs should not be able to use this!
Reimplemented from BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.
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virtual |
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virtual |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
- Returns
- The Expected Value of the Pdf (a ColumnVector with DIMENSION rows)
- Note
- No set functions here! This can be useful for analytic functions, but not for sample based representations!
- For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?
Reimplemented from BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.
Member Data Documentation
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private |
Definition at line 59 of file optimal_importance_density.h.
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private |
Definition at line 58 of file optimal_importance_density.h.
The documentation for this class was generated from the following file: