#include <pdf.h>
Public Member Functions | |
Pdf (unsigned int dimension=0) | |
Constructor. | |
virtual | ~Pdf () |
Destructor. | |
virtual Pdf< T > * | Clone () const =0 |
Pure virtual clone function. | |
virtual bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded). | |
virtual bool | SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf:. | |
virtual Probability | ProbabilityGet (const T &input) const |
Get the probability of a certain argument. | |
unsigned int | DimensionGet () const |
Get the dimension of the argument. | |
virtual void | DimensionSet (unsigned int dim) |
Set the dimension of the argument. | |
virtual T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. |
Definition at line 53 of file pdf.h.
( | unsigned int | dimension = 0 |
) | [inline] |
bool SampleFrom | ( | vector< Sample< T > > & | list_samples, | |
const unsigned int | num_samples, | |||
int | method = DEFAULT , |
|||
void * | args = NULL | |||
) | const [inline, virtual] |
Draw multiple samples from the Pdf (overloaded).
list_samples | list of samples that will contain result of sampling | |
num_samples | Number of Samples to be drawn (iid) | |
method | Sampling method to be used. Each sampling method is currently represented by a define statement, eg. define BOXMULLER 1 | |
args | Pointer to a struct representing extra sample arguments. "Sample Arguments" can be anything (the number of steps a gibbs-iterator should take, the interval width in MCMC, ... (or nothing), so it is hard to give a meaning to what exactly Sample Arguments should represent... |
Reimplemented in DiscretePdf, MCPdf, and Mixture.
bool SampleFrom | ( | Sample< T > & | one_sample, | |
int | method = DEFAULT , |
|||
void * | args = NULL | |||
) | const [inline, virtual] |
Draw 1 sample from the Pdf:.
There's no need to create a list for only 1 sample!
one_sample | sample that will contain result of sampling | |
method | Sampling method to be used. Each sampling method is currently represented by a define statement, eg. define BOXMULLER 1 | |
args | Pointer to a struct representing extra sample arguments |
Reimplemented in ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, MCPdf, Mixture, and Uniform.
Probability ProbabilityGet | ( | const T & | input | ) | const [inline, virtual] |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in ConditionalGaussian, DiscreteConditionalPdf, DiscretePdf, Gaussian, Mixture, and Uniform.
unsigned int DimensionGet | ( | ) | const [inline] |
void DimensionSet | ( | unsigned int | dim | ) | [inline, virtual] |
T ExpectedValueGet | ( | ) | const [inline, virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?
Reimplemented in FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf, Mixture, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
MatrixWrapper::SymmetricMatrix CovarianceGet | ( | ) | const [inline, virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf, Mixture, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.