#include <itkGaussianDistribution.h>

Inheritance diagram for itk::Statistics::GaussianDistribution:

Collaboration diagram for itk::Statistics::GaussianDistribution:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef SmartPointer< Self >	Pointer
typedef GaussianDistribution	Self
typedef ProbabilityDistribution	Superclass
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual double	EvaluateCDF (double x) const
virtual double	EvaluateCDF (double x, const ParametersType &) const
virtual double	EvaluateCDF (double x, double mean, double variance) const
virtual double	EvaluateInverseCDF (double p) const
virtual double	EvaluateInverseCDF (double p, const ParametersType &) const
virtual double	EvaluateInverseCDF (double p, double mean, double variance) const
virtual double	EvaluatePDF (double x) const
virtual double	EvaluatePDF (double x, const ParametersType &) const
virtual double	EvaluatePDF (double x, double mean, double variance) const
virtual double	GetMean () const
virtual const char *	GetNameOfClass () const
virtual SizeValueType	GetNumberOfParameters () const
virtual double	GetVariance () const
virtual bool	HasMean () const
virtual bool	HasVariance () const
virtual void	SetMean (double)
virtual void	SetVariance (double)
Static Public Member Functions
static double	CDF (double x)
static double	CDF (double x, const ParametersType &)
static double	CDF (double x, double mean, double variance)
static double	InverseCDF (double p, const ParametersType &)
static double	InverseCDF (double p, double mean, double variance)
static Pointer	New ()
static double	PDF (double x)
static double	PDF (double x, const ParametersType &)
static double	PDF (double x, double mean, double variance)

static double	InverseCDF (double p)
Protected Member Functions
	GaussianDistribution (void)
void	PrintSelf (std::ostream &os, Indent indent) const
virtual	~GaussianDistribution (void)
Private Member Functions
	GaussianDistribution (const Self &)
void	operator= (const Self &)

Detailed Description

GaussianDistribution class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.).

GaussianDistribution provides access to the probability density function (pdf), the cumulative distribution function (cdf), and the inverse cumulative distribution function for a Gaussian 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 provided. The static methods allow for optimized access to distributions when the distribution is known a priori to the algorithm.

GaussianDistributions 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).

GaussianDistributions can be used for Z-score statistical tests.

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.

Wiki Examples:

All Examples

Create a Gaussian distribution

Definition at line 60 of file itkGaussianDistribution.h.

Member Typedef Documentation

typedef SmartPointer< const Self > itk::Statistics::GaussianDistribution::ConstPointer

Reimplemented from itk::Statistics::ProbabilityDistribution.

Definition at line 68 of file itkGaussianDistribution.h.

typedef SmartPointer< Self > itk::Statistics::GaussianDistribution::Pointer

Reimplemented from itk::Statistics::ProbabilityDistribution.

Definition at line 67 of file itkGaussianDistribution.h.

typedef GaussianDistribution itk::Statistics::GaussianDistribution::Self

Standard class typedefs

Reimplemented from itk::Statistics::ProbabilityDistribution.

Definition at line 65 of file itkGaussianDistribution.h.

typedef ProbabilityDistribution itk::Statistics::GaussianDistribution::Superclass

Reimplemented from itk::Statistics::ProbabilityDistribution.

Definition at line 66 of file itkGaussianDistribution.h.

Constructor & Destructor Documentation

itk::Statistics::GaussianDistribution::GaussianDistribution ( void ) [protected]

virtual itk::Statistics::GaussianDistribution::~GaussianDistribution ( void ) [inline, protected, virtual]

Definition at line 235 of file itkGaussianDistribution.h.

itk::Statistics::GaussianDistribution::GaussianDistribution ( const Self & ) [private]

Member Function Documentation

static double itk::Statistics::GaussianDistribution::CDF ( double x ) [static]

Static method to evaluate the cumulative distribution function (cdf) of a standardized (mean zero, unit variance) Gaussian. The static method provides optimized access without requiring an instance of the class. Accuracy is approximately 10^-8.

static double itk::Statistics::GaussianDistribution::CDF	(	double	x,
		const ParametersType &
	)		`[static]`

Static method to evaluate the cumulative distribution function (cdf) of a Gaussian. The parameters of the distribution are passed as a parameter vector. The ordering of the parameters is (mean, variance). The static method provides optimized access without requiring an instance of the class.

static double itk::Statistics::GaussianDistribution::CDF	(	double	x,
		double	mean,
		double	variance
	)		`[static]`

Static method to evaluate the cumulative distribution function (cdf) of a Gaussian. The parameters of the distribution are passed as separate values. The static method provides optimized access without requiring an instance of the class.

virtual::itk::LightObject::Pointer itk::Statistics::GaussianDistribution::CreateAnother ( void ) const [virtual]

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::Object.

virtual double itk::Statistics::GaussianDistribution::EvaluateCDF ( double x ) const [virtual]

Evaluate the cumulative distribution function (cdf). The parameters of the distribution are assigned via SetParameters().

Implements itk::Statistics::ProbabilityDistribution.

virtual double itk::Statistics::GaussianDistribution::EvaluateCDF	(	double	x,
		const ParametersType &
	)		const `[virtual]`

Evaluate the cumulative distribution function (cdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (mean, variance).

Implements itk::Statistics::ProbabilityDistribution.

virtual double itk::Statistics::GaussianDistribution::EvaluateCDF	(	double	x,
		double	mean,
		double	variance
	)		const `[virtual]`

Evaluate the cumulative distribution function (cdf). The parameters of the distribution are passed as separate parameters.

virtual double itk::Statistics::GaussianDistribution::EvaluateInverseCDF ( double p ) const [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().

Implements itk::Statistics::ProbabilityDistribution.

virtual double itk::Statistics::GaussianDistribution::EvaluateInverseCDF	(	double	p,
		const ParametersType &
	)		const `[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. The ordering of the parameters is (mean, variance).

Implements itk::Statistics::ProbabilityDistribution.

virtual double itk::Statistics::GaussianDistribution::EvaluateInverseCDF	(	double	p,
		double	mean,
		double	variance
	)		const `[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 passed as separate parameters.

virtual double itk::Statistics::GaussianDistribution::EvaluatePDF ( double x ) const [virtual]

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

Implements itk::Statistics::ProbabilityDistribution.

virtual double itk::Statistics::GaussianDistribution::EvaluatePDF	(	double	x,
		const ParametersType &
	)		const `[virtual]`

Evaluate the probability density function (pdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (mean, variance).

Implements itk::Statistics::ProbabilityDistribution.

virtual double itk::Statistics::GaussianDistribution::EvaluatePDF	(	double	x,
		double	mean,
		double	variance
	)		const `[virtual]`

Evaluate the probability density function (pdf). The parameters of the distribution are passed as separate parameters.

virtual double itk::Statistics::GaussianDistribution::GetMean ( ) const [virtual]

Get the mean of the Gaussian distribution. Defaults to 0.0. The mean is stored in position 0 of the parameters vector.

Implements itk::Statistics::ProbabilityDistribution.

virtual const char* itk::Statistics::GaussianDistribution::GetNameOfClass ( ) const [virtual]

Strandard macros

Reimplemented from itk::Statistics::ProbabilityDistribution.

virtual SizeValueType itk::Statistics::GaussianDistribution::GetNumberOfParameters ( ) const [inline, virtual]

Return the number of parameters. For a univariate Gaussian, this is 2 (mean, variance).

Implements itk::Statistics::ProbabilityDistribution.

Definition at line 78 of file itkGaussianDistribution.h.

virtual double itk::Statistics::GaussianDistribution::GetVariance ( ) const [virtual]

Get the variance of the Gaussian distribution. Defaults to 1.0. The variance is stored in position 1 of the parameters vector.

Implements itk::Statistics::ProbabilityDistribution.

virtual bool itk::Statistics::GaussianDistribution::HasMean ( ) const [inline, virtual]

Does this distribution have a mean?

Implements itk::Statistics::ProbabilityDistribution.

Definition at line 133 of file itkGaussianDistribution.h.

virtual bool itk::Statistics::GaussianDistribution::HasVariance ( ) const [inline, virtual]

Does this distribution have a variance?

Implements itk::Statistics::ProbabilityDistribution.

Definition at line 145 of file itkGaussianDistribution.h.

static double itk::Statistics::GaussianDistribution::InverseCDF ( double p ) [static]

Static method to evaluate the inverse cumulative distribution function of a standardized (mean zero, unit variance) Gaussian. The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0.

THis implementation was provided by Robert W. Cox from the Biophysics Research Institute at the Medical College of Wisconsin. This function is based off of a rational polynomial approximation to the inverse Gaussian CDF which can be found in M. Abramowitz and I.A. Stegun. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. John Wiley & Sons. New York. Equation 26.2.23. pg. 933. 1972.

Since the initial approximation only provides an estimate within 4.5 E-4 of the true value, 3 Newton-Raphson interations are used to refine the approximation. Accuracy is approximately 10^-8.

Let, Q(x) = (1/sqrt(2*pi)) Int_{x}^{infinity} e^{-t^2/2} dt = 0.5 * erfc(x/sqrt(2))

Given p, this function computes x such that Q(x) = p, for 0 < p < 1

Note that the Gaussian CDF is defined as P(x) = (1/sqrt(2*pi)) Int_{-infinity}{x} e^{-t^2/2} dt = 1 - Q(x)

This function has been modified to compute the inverse of P(x) instead of Q(x).

static double itk::Statistics::GaussianDistribution::InverseCDF	(	double	p,
		const ParametersType &
	)		`[static]`

Static method to evaluate the inverse cumulative distribution function of a Gaussian. The parameters of the distribution are passed as a parameter vector. The ordering of the parameters is (mean, variance). The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0

static double itk::Statistics::GaussianDistribution::InverseCDF	(	double	p,
		double	mean,
		double	variance
	)		`[static]`

Static method to evaluate the inverse cumulative distribution function of a Gaussian. The parameters of the distribution are passed as separate values. The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0

static Pointer itk::Statistics::GaussianDistribution::New ( ) [static]

Method for creation through the object factory.

Reimplemented from itk::Object.

void itk::Statistics::GaussianDistribution::operator= ( const Self & ) [private]

Mutex lock to protect modification to the reference count

Reimplemented from itk::Statistics::ProbabilityDistribution.

static double itk::Statistics::GaussianDistribution::PDF ( double x ) [static]

Static method to evaluate the probability density function (pdf) of a standardized (mean zero, unit variance) Gaussian. The static method provides optimized access without requiring an instance of the class.

static double itk::Statistics::GaussianDistribution::PDF	(	double	x,
		const ParametersType &
	)		`[static]`

Static method to evaluate the probability density function (pdf) of a Gaussian. The parameters of the distribution are passed as a parameter vector. The ordering of the parameters is (mean, variance). The static method provides optimized access without requiring an instance of the class.

static double itk::Statistics::GaussianDistribution::PDF	(	double	x,
		double	mean,
		double	variance
	)		`[static]`

Static method to evaluate the probability density function (pdf) of a Gaussian. The parameters of the distribution are passed as separate values. The static method provides optimized access without requiring an instance of the class.

void itk::Statistics::GaussianDistribution::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)		const `[protected, virtual]`

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::Statistics::ProbabilityDistribution.

virtual void itk::Statistics::GaussianDistribution::SetMean ( double ) [virtual]

Set the mean of the Gaussian distribution. Defaults to 0.0. The mean is stored in position 0 of the parameters vector.

virtual void itk::Statistics::GaussianDistribution::SetVariance ( double ) [virtual]

Set the variance of the Gaussian distribution. Defaults to 1.0. The variance is stored in position 1 of the parameters vector.

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

itkGaussianDistribution.h

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Member Functions

Detailed Description

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation