TNormale Class Reference

#include <TNormale.h>

Inheritance diagram for TNormale:

Collaboration diagram for TNormale:

Public Member Functions
	TNormale ()
	TNormale (TVectorD M, TMatrixD A)
	Constructor of the class. M is the mean vector we want to attribute to our gaussian vector and to its matrix of "standard deviation/correlation".
	~TNormale ()
	Destructor.
Double_t	eval (TVectorD x)
	Returns the value of the density along x of a multidimensional normal law.
Double_t	eval (Double_t x)
	Returns the value of the density along x of a real normal law.
void	acceptationRejet ()
	Method for accepting-refusing to simulate a gaussian with a Cauchy law as instrumental distribution.
Double_t	getObs ()
void	simulationPolaire ()
	method of simulation of a centered reduced real normal law by the algorithme of Box-Muller
TVectorD	vectCentreReduit ()
	Simulates a centered reduced gaussian vector.
TVectorD	getObsMultiDim ()
	Simulates a given gaussian vector from the decomposition of Choleski
void	simVectGaussien ()
	ClassDef (TNormale, ID_SAMPLER)
Public Member Functions inherited from TDistribution
	TDistribution ()
	TDistribution (TVectorD M, TMatrixD A)
void	initialisation (TVectorD M, TMatrixD A)
	~TDistribution ()
TNtupleD *	getTuple ()
void	setTaille (Int_t n)
TRandom3 *	getRandom ()
Int_t	getTaille ()
Int_t	getDim ()
TVectorD	getMean ()
TVectorD	getSigma ()
TMatrixD	getMatEcartTypeCorrelation ()
TMatrixD	getMatCorrelation ()
TMatrixD	getMatCovariance ()
void	setMean (TVectorD M)
void	setMatEcartTypeCorrelation (TMatrixD A)
void	setProb (Double_t proba)
Double_t	getProb ()
void	simulation ()
void	verification ()
	ClassDef (TDistribution, ID_SAMPLER)

Additional Inherited Members
Protected Attributes inherited from TDistribution
TRandom3 *	_rdm
	Generator of random number.
Double_t	_dprob
	Weight of the distribution.
Int_t	_taille
	Sample size.
TNtupleD *	_vect
	Tuple where simulated samples are stored.
Int_t	_dim
	Dimension of the distribution.
TVectorD	_vectMean
	Mean value of the distribution.
TMatrixD	_matEcartTypeCorr
	"_matEcartTypeCorr" represents the along the diagonal and elsewhere. Be careful the matrix must be symmetric

Constructor & Destructor Documentation

TNormale() [1/2]

TNormale::TNormale

(

)

TNormale() [2/2]

TNormale::TNormale	(	TVectorD	M,
		TMatrixD	A
	)

Constructor of the class. M is the mean vector we want to attribute to our gaussian vector and to its matrix of "standard deviation/correlation".

References TDistribution::_dim, TDistribution::_vect, and TDistribution::initialisation().

~TNormale()

TNormale::~TNormale

(

)

Destructor.

Member Function Documentation

acceptationRejet()

void TNormale::acceptationRejet

(

)

Method for accepting-refusing to simulate a gaussian with a Cauchy law as instrumental distribution.

References TDistribution::_matEcartTypeCorr, TDistribution::_rdm, TDistribution::_taille, TDistribution::_vect, TDistribution::_vectMean, and TDistribution::getDim().

Referenced by TMelange::simulationMelange().

ClassDef()

TNormale::ClassDef	(	TNormale	,
		ID_SAMPLER
	)

eval() [1/2]

Double_t TNormale::eval

(

Double_t

x

)

Returns the value of the density along x of a real normal law.

References TDistribution::_dim, TDistribution::_matEcartTypeCorr, and TDistribution::_vectMean.

eval() [2/2]

Double_t TNormale::eval

(

TVectorD

x

)

Returns the value of the density along x of a multidimensional normal law.

References TDistribution::_dim, eval(), TDistribution::getMatCovariance(), TDistribution::getMean(), and m.

Referenced by TMelange::discrimination(), TMelange::eval(), TMelange::eval(), eval(), TMelange::LoiDeZ(), TMCMC::NKCSampling2(), TMCMC::randomWalkMetropolisSampling(), TMCMC::varTimeMetropolisSampling(), and TMCMC::varTimeMetropolisSampling().

getObs()

Double_t TNormale::getObs

(

)

References TDistribution::_matEcartTypeCorr, TDistribution::_rdm, TDistribution::_vectMean, and TDistribution::getDim().

Referenced by TMelange::getObs(), TMCMC::getObs(), TMCMC::gibbsSampling(), TMCMC::gibbsSampling(), TMCMC::varTimeMetropolisSampling(), TMCMC::varTimeMetropolisSampling(), and vectCentreReduit().

getObsMultiDim()

TVectorD TNormale::getObsMultiDim

(

)

Simulates a given gaussian vector from the decomposition of Choleski

References TDistribution::_dim, TDistribution::getMatCovariance(), TDistribution::getMean(), m, and vectCentreReduit().

Referenced by TMCMC::gibbsSampling(), TMCMC::NKCSampling(), TMCMC::NKCSampling2(), TMCMC::randomWalkMetropolisSampling(), TMCMC::randomWalkMetropolisSampling(), and simVectGaussien().

simulationPolaire()

void TNormale::simulationPolaire

(

)

method of simulation of a centered reduced real normal law by the algorithme of Box-Muller

References TDistribution::_matEcartTypeCorr, TDistribution::_rdm, TDistribution::_taille, TDistribution::_vect, TDistribution::_vectMean, and TDistribution::getDim().

simVectGaussien()

void TNormale::simVectGaussien

(

)

References TDistribution::_dim, TDistribution::_taille, TDistribution::_vect, and getObsMultiDim().

vectCentreReduit()

TVectorD TNormale::vectCentreReduit

(

)

Simulates a centered reduced gaussian vector.

References TDistribution::_dim, TDistribution::getMatEcartTypeCorrelation(), TDistribution::getMean(), getObs(), m, TDistribution::setMatEcartTypeCorrelation(), and TDistribution::setMean().

Referenced by getObsMultiDim().