cmp::distribution::MultivariateStudentDistribution Class Reference

#include <distribution.h>

Inheritance diagram for cmp::distribution::MultivariateStudentDistribution:

Collaboration diagram for cmp::distribution::MultivariateStudentDistribution:

Public Member Functions
template<typename LDLTDerived >
	MultivariateStudentDistribution (const Eigen::Ref< const Eigen::VectorXd > &mean, const Eigen::LDLT< LDLTDerived > &ldltDecomposition, double nu)
	MultivariateStudentDistribution (const Eigen::Ref< const Eigen::VectorXd > &mean, const Eigen::Ref< const Eigen::MatrixXd > &cov, double nu)
	MultivariateStudentDistribution ()=default
double	logPDF (const Eigen::Ref< const Eigen::VectorXd > &x) const
double	logJumpPDF (const Eigen::Ref< const Eigen::VectorXd > &jump)
double	squaredMahalanobis (const Eigen::Ref< const Eigen::VectorXd > &jump) const
Eigen::VectorXd	sample (std::default_random_engine &rng, const double &gamma=1.0)
Eigen::VectorXd	get () const
void	set (const Eigen::Ref< const Eigen::VectorXd > &x)
void	setMean (const Eigen::Ref< const Eigen::VectorXd > &mean)
void	setLdltDecomposition (const Eigen::LDLT< Eigen::MatrixXd > &ldltDecomposition)
void	setDoFs (double nu)
Eigen::MatrixXd	toCanonical (const Eigen::MatrixXd &x) const
Eigen::MatrixXd	toPhysical (const Eigen::MatrixXd &z) const
size_t	dimension () const
Public Member Functions inherited from cmp::distribution::ProposalDistribution< MultivariateStudentDistribution >
double	logJumpPDF (const Eigen::Ref< const Eigen::VectorXd > &jump)
Eigen::VectorXd	sample (std::default_random_engine &rng, const double &gamma)
Eigen::VectorXd	sample (std::default_random_engine &rng)
double	squaredMahalanobis (const Eigen::Ref< const Eigen::VectorXd > &jump) const
Eigen::VectorXd	get () const
void	set (const Eigen::Ref< const Eigen::VectorXd > &x)
Public Member Functions inherited from cmp::distribution::MultivariateDistribution< Derived >
double	logPDF (const Eigen::Ref< const Eigen::VectorXd > &x) const
	Computes the joint log probability density function (log-PDF) of the distribution.
Eigen::VectorXd	sample (std::default_random_engine &rng)
	Draws a single vector sample from the joint distribution.
Eigen::MatrixXd	toCanonical (const Eigen::MatrixXd &x) const
	Transforms physical samples to standard canonical space.
Eigen::MatrixXd	fromCanonical (const Eigen::MatrixXd &x) const
	Transforms standard canonical samples back to physical space.
size_t	dimension () const
	Returns the dimensionality of the multivariate space.

Static Public Member Functions
static double	logPDF (const Eigen::Ref< const Eigen::VectorXd > &res, const Eigen::LDLT< Eigen::MatrixXd > &ldltDecomposition, const double &nu)
static MultivariateStudentDistribution	canonical (const size_t dim, const double nu)

Private Attributes
Eigen::VectorXd	mean_
	Mean vector.
Eigen::LDLT< Eigen::MatrixXd >	ldltDecomposition_
	LDLT decomposition of the covariance scale matrix.
double	dofs_
	Degrees of freedom parameter (nu).
std::normal_distribution< double >	distN_
	Normal distribution helper for coordinate sampling.

Constructor & Destructor Documentation

MultivariateStudentDistribution() [1/3]

template<typename LDLTDerived >

cmp::distribution::MultivariateStudentDistribution::MultivariateStudentDistribution ( const Eigen::Ref< const Eigen::VectorXd > &  mean, const Eigen::LDLT< LDLTDerived > &  ldltDecomposition, double  nu  )

inline

MultivariateStudentDistribution() [2/3]

cmp::distribution::MultivariateStudentDistribution::MultivariateStudentDistribution ( const Eigen::Ref< const Eigen::VectorXd > &  mean, const Eigen::Ref< const Eigen::MatrixXd > &  cov, double  nu  )

inline

MultivariateStudentDistribution() [3/3]

cmp::distribution::MultivariateStudentDistribution::MultivariateStudentDistribution ( )

default

Member Function Documentation

canonical()

static MultivariateStudentDistribution cmp::distribution::MultivariateStudentDistribution::canonical ( const size_t  dim, const double  nu  )

inlinestatic

dimension()

size_t cmp::distribution::MultivariateStudentDistribution::dimension ( ) const

inline

get()

Eigen::VectorXd cmp::distribution::MultivariateStudentDistribution::get ( ) const

inline

logJumpPDF()

double cmp::distribution::MultivariateStudentDistribution::logJumpPDF ( const Eigen::Ref< const Eigen::VectorXd > &  jump )

inline

logPDF() [1/2]

static double cmp::distribution::MultivariateStudentDistribution::logPDF ( const Eigen::Ref< const Eigen::VectorXd > &  res, const Eigen::LDLT< Eigen::MatrixXd > &  ldltDecomposition, const double &  nu  )

inlinestatic

logPDF() [2/2]

double cmp::distribution::MultivariateStudentDistribution::logPDF ( const Eigen::Ref< const Eigen::VectorXd > &  x ) const

inline

sample()

Eigen::VectorXd cmp::distribution::MultivariateStudentDistribution::sample ( std::default_random_engine &  rng, const double &  gamma = 1.0  )

inline

set()

void cmp::distribution::MultivariateStudentDistribution::set ( const Eigen::Ref< const Eigen::VectorXd > &  x )

inline

setDoFs()

void cmp::distribution::MultivariateStudentDistribution::setDoFs ( double  nu )

inline

setLdltDecomposition()

void cmp::distribution::MultivariateStudentDistribution::setLdltDecomposition ( const Eigen::LDLT< Eigen::MatrixXd > &  ldltDecomposition )

inline

setMean()

void cmp::distribution::MultivariateStudentDistribution::setMean ( const Eigen::Ref< const Eigen::VectorXd > &  mean )

inline

squaredMahalanobis()

double cmp::distribution::MultivariateStudentDistribution::squaredMahalanobis ( const Eigen::Ref< const Eigen::VectorXd > &  jump ) const

inline

toCanonical()

Eigen::MatrixXd cmp::distribution::MultivariateStudentDistribution::toCanonical ( const Eigen::MatrixXd &  x ) const

inline

toPhysical()

Eigen::MatrixXd cmp::distribution::MultivariateStudentDistribution::toPhysical ( const Eigen::MatrixXd &  z ) const

inline

Member Data Documentation

distN_

std::normal_distribution<double> cmp::distribution::MultivariateStudentDistribution::distN_

private

Normal distribution helper for coordinate sampling.

dofs_

double cmp::distribution::MultivariateStudentDistribution::dofs_

private

Degrees of freedom parameter (nu).

ldltDecomposition_

Eigen::LDLT<Eigen::MatrixXd> cmp::distribution::MultivariateStudentDistribution::ldltDecomposition_

private

LDLT decomposition of the covariance scale matrix.

mean_

Eigen::VectorXd cmp::distribution::MultivariateStudentDistribution::mean_

private

Mean vector.

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

• include/distribution.h