ITK  5.3.0 Insight Toolkit
itk::Statistics::ProbabilityDistribution Class Referenceabstract

#include <itkProbabilityDistribution.h>

Inheritance diagram for itk::Statistics::ProbabilityDistribution:
Collaboration diagram for itk::Statistics::ProbabilityDistribution:

## Public Types

using ConstPointer = SmartPointer< const Self >

using ParametersType = Array< double >

using Pointer = SmartPointer< Self >

using Self = ProbabilityDistribution

using Superclass = Object

Public Types inherited from itk::Object
using ConstPointer = SmartPointer< const Self >

using Pointer = SmartPointer< Self >

using Self = Object

using Superclass = LightObject

Public Types inherited from itk::LightObject
using ConstPointer = SmartPointer< const Self >

using Pointer = SmartPointer< Self >

using Self = LightObject

## Public Member Functions

virtual double EvaluateCDF (double x) const =0

virtual double EvaluateCDF (double x, const ParametersType &) const =0

virtual double EvaluateInverseCDF (double p) const =0

virtual double EvaluateInverseCDF (double p, const ParametersType &) const =0

virtual double EvaluatePDF (double x) const =0

virtual double EvaluatePDF (double x, const ParametersType &) const =0

virtual double GetMean () const =0

virtual const char * GetNameOfClass () const

virtual SizeValueType GetNumberOfParameters () const =0

virtual const ParametersTypeGetParameters () const

virtual double GetVariance () const =0

virtual bool HasMean () const =0

virtual bool HasVariance () const =0

virtual void SetParameters (const ParametersType &params)

Public Member Functions inherited from itk::Object
unsigned long AddObserver (const EventObject &event, Command *)

unsigned long AddObserver (const EventObject &event, Command *) const

unsigned long AddObserver (const EventObject &event, std::function< void(const EventObject &)> function) const

LightObject::Pointer CreateAnother () const override

virtual void DebugOff () const

virtual void DebugOn () const

CommandGetCommand (unsigned long tag)

bool GetDebug () const

virtual ModifiedTimeType GetMTime () const

virtual const TimeStampGetTimeStamp () const

bool HasObserver (const EventObject &event) const

void InvokeEvent (const EventObject &)

void InvokeEvent (const EventObject &) const

virtual void Modified () const

void Register () const override

void RemoveAllObservers ()

void RemoveObserver (unsigned long tag)

void SetDebug (bool debugFlag) const

void SetReferenceCount (int) override

void UnRegister () const noexcept override

virtual void SetObjectName (std::string _arg)

virtual const std::string & GetObjectName () const

Public Member Functions inherited from itk::LightObject
Pointer Clone () const

virtual void Delete ()

virtual int GetReferenceCount () const

void Print (std::ostream &os, Indent indent=0) const

## Protected Member Functions

void PrintSelf (std::ostream &os, Indent indent) const override

ProbabilityDistribution ()

~ProbabilityDistribution () override

Protected Member Functions inherited from itk::Object
Object ()

bool PrintObservers (std::ostream &os, Indent indent) const

virtual void SetTimeStamp (const TimeStamp &timeStamp)

~Object () override

Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer InternalClone () const

LightObject ()

virtual void PrintHeader (std::ostream &os, Indent indent) const

virtual void PrintTrailer (std::ostream &os, Indent indent) const

virtual ~LightObject ()

## Protected Attributes

ParametersType m_Parameters

Protected Attributes inherited from itk::LightObject
std::atomic< int > m_ReferenceCount

## Additional Inherited Members

Static Public Member Functions inherited from itk::Object
static bool GetGlobalWarningDisplay ()

static void GlobalWarningDisplayOff ()

static void GlobalWarningDisplayOn ()

static Pointer New ()

static void SetGlobalWarningDisplay (bool val)

Static Public Member Functions inherited from itk::LightObject
static void BreakOnError ()

static Pointer New ()

## Detailed Description

ProbabilityDistribution class defines common interface for statistical distributions (pdfs, cdfs, etc.).

ProbabilityDistribution defines a common interface for parametric and non-parametric distributions. ProbabilityDistribution provides access to the probability density function (pdf), the cumulative distribution function (cdf), and the inverse cumulative distribution function.

ProbabilityDistribution also defines an abstract interface for setting parameters of distribution (mean/variance for a Gaussian, degrees of freedom for Student-t, etc.).

Note that nonparametric subclasses of ProbabilityDistribution are possible. For instance, a nonparametric implementation may use a histogram or kernel density function to model the distribution.

The EvaluatePDF(), EvaluateCDF, EvaluateInverseCDF() methods are all virtual, allowing algorithms to be written with an abstract interface to a distribution (with said distribution provided to the algorithm at run-time). Static methods, not requiring an instance of the distribution, are also allowed. The static methods allow for optimized access to distributions when the distribution is known a priori to the algorithm.

ProbabilityDistributions are univariate. Multivariate versions may be provided under a separate superclass (since the parameters to the pdf and cdf would have to be vectors not scalars). Perhaps this class will be named MultivariateProbabilityDistribution.

ProbabilityDistributions can be used for standard statistical tests: Z-scores, t-tests, chi-squared tests, F-tests, etc.

Note
This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://commonfund.nih.gov/bioinformatics.

Definition at line 72 of file itkProbabilityDistribution.h.

## ◆ ConstPointer

 using itk::Statistics::ProbabilityDistribution::ConstPointer = SmartPointer

Definition at line 81 of file itkProbabilityDistribution.h.

## ◆ ParametersType

 using itk::Statistics::ProbabilityDistribution::ParametersType = Array

Type of the parameter vector.

Definition at line 87 of file itkProbabilityDistribution.h.

## ◆ Pointer

Definition at line 80 of file itkProbabilityDistribution.h.

## ◆ Self

Standard class type aliases

Definition at line 78 of file itkProbabilityDistribution.h.

## ◆ Superclass

Definition at line 79 of file itkProbabilityDistribution.h.

## ◆ ProbabilityDistribution()

 itk::Statistics::ProbabilityDistribution::ProbabilityDistribution ( )
protected

## ◆ ~ProbabilityDistribution()

 itk::Statistics::ProbabilityDistribution::~ProbabilityDistribution ( )
overrideprotected

## ◆ EvaluateCDF() [1/2]

 virtual double itk::Statistics::ProbabilityDistribution::EvaluateCDF ( double x ) const
pure virtual

Evaluate the cumulative distribution function (cdf). The parameters of the distribution are assigned via SetParameters(). See concrete subclasses for the ordering of parameters.

## ◆ EvaluateCDF() [2/2]

 virtual double itk::Statistics::ProbabilityDistribution::EvaluateCDF ( double x, const ParametersType & ) const
pure virtual

Evaluate the cumulative distribution function (cdf). The parameters for the distribution are passed as a parameters vector. See concrete subclasses for the ordering of parameters.

## ◆ EvaluateInverseCDF() [1/2]

 virtual double itk::Statistics::ProbabilityDistribution::EvaluateInverseCDF ( double p ) const
pure virtual

Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters of the distribution are assigned via SetParameters(). See concrete subclasses for the ordering of parameters.

## ◆ EvaluateInverseCDF() [2/2]

 virtual double itk::Statistics::ProbabilityDistribution::EvaluateInverseCDF ( double p, const ParametersType & ) const
pure virtual

Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters for the distribution are passed as a parameters vector. See concrete subclasses for the ordering of parameters.

## ◆ EvaluatePDF() [1/2]

 virtual double itk::Statistics::ProbabilityDistribution::EvaluatePDF ( double x ) const
pure virtual

Evaluate the probability density function (pdf). The parameters of the distribution are assigned via SetParameters().

## ◆ EvaluatePDF() [2/2]

 virtual double itk::Statistics::ProbabilityDistribution::EvaluatePDF ( double x, const ParametersType & ) const
pure virtual

Evaluate the probability density function (pdf). The parameters for the distribution are passed as a parameters vector. See concrete subclasses for the ordering of parameters.

## ◆ GetMean()

 virtual double itk::Statistics::ProbabilityDistribution::GetMean ( ) const
pure virtual

Get the mean of the distribution. If the mean does not exist, then quiet_NaN may is returned.

## ◆ GetNameOfClass()

 virtual const char* itk::Statistics::ProbabilityDistribution::GetNameOfClass ( ) const
virtual

Standard macros

Reimplemented from itk::Object.

## ◆ GetNumberOfParameters()

 virtual SizeValueType itk::Statistics::ProbabilityDistribution::GetNumberOfParameters ( ) const
pure virtual

Return the number of parameters that describe the distribution. For nonparametric distributions, this will be a function of the number of samples.

## ◆ GetParameters()

 virtual const ParametersType& itk::Statistics::ProbabilityDistribution::GetParameters ( ) const
virtual

Get the parameters of the distribution. See concrete subclasses for the order of parameters. Subclasses may provide convenience methods for setting parameters, i.e. SetDegreesOfFreedom(), etc.

## ◆ GetVariance()

 virtual double itk::Statistics::ProbabilityDistribution::GetVariance ( ) const
pure virtual

Get the variance of the distribution. If the variance does not exist, then quiet_NaN is returned.

## ◆ HasMean()

 virtual bool itk::Statistics::ProbabilityDistribution::HasMean ( ) const
pure virtual

Does this distribution have a mean?

## ◆ HasVariance()

 virtual bool itk::Statistics::ProbabilityDistribution::HasVariance ( ) const
pure virtual

Does this distribution have a variance?

## ◆ PrintSelf()

 void itk::Statistics::ProbabilityDistribution::PrintSelf ( std::ostream & os, Indent indent ) const
overrideprotectedvirtual

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.

Reimplemented from itk::Object.

Reimplemented in itk::Statistics::TDistribution.

## ◆ SetParameters()

 virtual void itk::Statistics::ProbabilityDistribution::SetParameters ( const ParametersType & params )
virtual

Set the parameters of the distribution. See concrete subclasses for the order of the parameters. Subclasses may provide convenience methods for setting parameters, i.e. SetDegreesOfFreedom(), etc.

## ◆ m_Parameters

 ParametersType itk::Statistics::ProbabilityDistribution::m_Parameters
protected

Definition at line 167 of file itkProbabilityDistribution.h.

The documentation for this class was generated from the following file: