cmp::distribution::MultivariateDistribution< Derived > Class Template Reference

CRTP base class for all multivariate probability distributions. More...

#include <distribution.h>

Inheritance diagram for cmp::distribution::MultivariateDistribution< Derived >:

Public Member Functions
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.

Detailed Description

template<typename Derived>
class cmp::distribution::MultivariateDistribution< Derived >

CRTP base class for all multivariate probability distributions.

Mathematical Formulation Defines a probability distribution over a random vector \(\mathbf{X} \in \mathbb{R}^d\) with joint probability density function \(p(\mathbf{x})\). Provides static polymorphic interfaces for:

• Joint log-likelihood: \(\log p(\mathbf{x})\)
• Vector sampling: drawing \(\mathbf{x} \sim p(\mathbf{x})\)

• Canonical transformations (bijective mapping to/from standard multivariate space).

  Implementation Algorithm Uses the Curiously Recurring Template Pattern (CRTP) to implement static polymorphism.

Member Function Documentation

dimension()

template<typename Derived >

size_t cmp::distribution::MultivariateDistribution< Derived >::dimension ( ) const

inline

Returns the dimensionality of the multivariate space.

fromCanonical()

template<typename Derived >

Eigen::MatrixXd cmp::distribution::MultivariateDistribution< Derived >::fromCanonical ( const Eigen::MatrixXd &  x ) const

inline

Transforms standard canonical samples back to physical space.

Parameters

x	Matrix of canonical samples.

Returns

Matrix of physical samples.

logPDF()

template<typename Derived >

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

inline

Computes the joint log probability density function (log-PDF) of the distribution.

Parameters

x	Vector point at which to evaluate the log-PDF.

Returns

Evaluated log-PDF.

sample()

template<typename Derived >

Eigen::VectorXd cmp::distribution::MultivariateDistribution< Derived >::sample ( std::default_random_engine &  rng )

inline

Draws a single vector sample from the joint distribution.

Parameters

rng	Random number engine.

Returns

Random vector sample.

toCanonical()

template<typename Derived >

Eigen::MatrixXd cmp::distribution::MultivariateDistribution< Derived >::toCanonical ( const Eigen::MatrixXd &  x ) const

inline

Transforms physical samples to standard canonical space.

Parameters

x	Matrix of physical samples (each row is a sample).

Returns

Matrix of canonical samples.

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

• include/distribution.h