Optimal importance density for Nonlinear Gaussian SS Models. More...
#include <optimal_importance_density.h>
Public Member Functions | |
| virtual SymmetricMatrix | CovarianceGet () const |
| Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
| virtual Matrix | dfGet (int i) const |
| virtual ColumnVector | ExpectedValueGet () const |
| Get the expected value E[x] of the pdf. | |
| OptimalImportanceDensity (AnalyticConditionalGaussian *SystemPdf, LinearAnalyticConditionalGaussian *MeasPdf) | |
| Constructor. | |
| virtual | ~OptimalImportanceDensity () |
| Destructor. | |
Private Attributes | |
| LinearAnalyticConditionalGaussian * | _MeasPdf |
| AnalyticConditionalGaussian * | _SystemPdf |
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:
-
SystemPdf MeasPdf
| virtual BFL::OptimalImportanceDensity::~OptimalImportanceDensity | ( | ) | [virtual] |
Destructor.
Member Function Documentation
| virtual SymmetricMatrix BFL::OptimalImportanceDensity::CovarianceGet | ( | ) | const [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 >.
| virtual Matrix BFL::OptimalImportanceDensity::dfGet | ( | int | i | ) | const [virtual] |
| virtual ColumnVector BFL::OptimalImportanceDensity::ExpectedValueGet | ( | ) | const [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
Definition at line 59 of file optimal_importance_density.h.
Definition at line 58 of file optimal_importance_density.h.
The documentation for this class was generated from the following file: