muq::Modeling::Gamma Class Reference

#include <Gamma.h>

Inheritance diagram for muq::Modeling::Gamma:

Detailed Description

Defines the Gamma distribution, which has probability density function

\[ \pi(x) = \frac{\beta^\alpha}{\Gamma(\alpha)}x^{\alpha-1}\exp\left(-\beta x\right), \]

where \(\alpha\) and \(\beta\) are parameters in the distribution. For multivariate \(x\), it is assumed that all components of \(x\) are independent. However, each component can have different parameters \(\alpha\) and \(\beta\), which are specified as vectors in the constructor.

Definition at line 18 of file Gamma.h.

Public Member Functions
	Gamma (double alphaIn, double betaIn)
	Gamma (Eigen::VectorXd const &alphaIn, Eigen::VectorXd const &betaIn)
virtual	~Gamma ()=default
Public Member Functions inherited from muq::Modeling::Distribution
	Distribution (unsigned int varSizeIn, Eigen::VectorXi const &hyperSizesIn=Eigen::VectorXi())
virtual	~Distribution ()=default
virtual double	LogDensity (ref_vector< Eigen::VectorXd > const &inputs)
	Evaluate the log-density. More...
virtual double	LogDensity (std::vector< Eigen::VectorXd > const &inputs)
	VARIADIC_TO_REFVECTOR (LogDensity, Eigen::VectorXd, double)
virtual Eigen::VectorXd	GradLogDensity (unsigned int wrt, std::vector< Eigen::VectorXd > const &inputs)
template<typename... Args>
Eigen::VectorXd	GradLogDensity (unsigned int wrt, Args... args)
virtual Eigen::VectorXd	ApplyLogDensityHessian (unsigned int const inWrt1, unsigned int const inWrt2, std::vector< Eigen::VectorXd > const &input, Eigen::VectorXd const &vec)
Eigen::VectorXd	Sample (ref_vector< Eigen::VectorXd > const &inputs)
	Sample the distribution. More...
Eigen::VectorXd	Sample (std::vector< Eigen::VectorXd > const &inputs)
Eigen::VectorXd	Sample ()
	Sample the distribution with no inputs. More...
	VARIADIC_TO_REFVECTOR (Sample, Eigen::VectorXd, Eigen::VectorXd)
std::shared_ptr< Density >	AsDensity ()
	Returns a density built from this distribution. More...
std::shared_ptr< RandomVariable >	AsVariable ()
	Returns a random variable built from this distribution. More...

Static Public Member Functions
static std::shared_ptr< Gamma >	FromMoments (Eigen::VectorXd const &mean, Eigen::VectorXd const &var)

Public Attributes
const Eigen::VectorXd	alpha
const Eigen::VectorXd	beta
Public Attributes inherited from muq::Modeling::Distribution
const unsigned int	varSize
const Eigen::VectorXi	hyperSizes

Constructor & Destructor Documentation

Gamma() [1/2]

Gamma::Gamma	(	double	alphaIn,
		double	betaIn
	)

Definition at line 15 of file Gamma.cpp.

Gamma() [2/2]

Gamma::Gamma	(	Eigen::VectorXd const &	alphaIn,
		Eigen::VectorXd const &	betaIn
	)

Definition at line 8 of file Gamma.cpp.

~Gamma()

virtual muq::Modeling::Gamma::~Gamma ( )

virtualdefault

Member Function Documentation

ApplyLogDensityHessian()

Eigen::VectorXd Gamma::ApplyLogDensityHessian ( unsigned int const  inWrt1, unsigned int const  inWrt2, ref_vector< Eigen::VectorXd > const &  input, Eigen::VectorXd const &  vec  )

overrideprivatevirtual

Reimplemented from muq::Modeling::Distribution.

Definition at line 66 of file Gamma.cpp.

References alpha.

ComputeConstant()

double Gamma::ComputeConstant ( Eigen::VectorXd const &  alphaIn, Eigen::VectorXd const &  betaIn  )

staticprivate

Definition at line 19 of file Gamma.cpp.

References logConst.

FromMoments()

std::shared_ptr< Gamma > Gamma::FromMoments ( Eigen::VectorXd const &  mean, Eigen::VectorXd const &  var  )

static

Creates a Gamma distribution given a mean and variance, which are converted to alpha and beta.

Definition at line 87 of file Gamma.cpp.

References alpha, and beta.

GradLogDensity()

Eigen::VectorXd Gamma::GradLogDensity ( unsigned int  wrt, ref_vector< Eigen::VectorXd > const &  inputs  )

overrideprivatevirtual

Reimplemented from muq::Modeling::Distribution.

Definition at line 49 of file Gamma.cpp.

References alpha, and beta.

LogDensityImpl()

double Gamma::LogDensityImpl ( ref_vector< Eigen::VectorXd > const &  inputs )

overrideprivatevirtual

Implement the log-density.

If known, the log-density should be implemented by a child. If it is not overridden then the default behavior is to return negative infinity (-1.0*std::numeric_limits<double>::infinity()).

Parameters

[in]

inputs

the vector of inputs to the log-density

Returns

The log density

Reimplemented from muq::Modeling::Distribution.

Definition at line 29 of file Gamma.cpp.

References alpha, beta, and logConst.

SampleImpl()

Eigen::VectorXd Gamma::SampleImpl ( ref_vector< Eigen::VectorXd > const &  inputs )

overrideprivatevirtual

Sample the distribution.

Reimplemented from muq::Modeling::Distribution.

Definition at line 40 of file Gamma.cpp.

References alpha, and beta.

Member Data Documentation

alpha

const Eigen::VectorXd muq::Modeling::Gamma::alpha

Definition at line 35 of file Gamma.h.

Referenced by ApplyLogDensityHessian(), FromMoments(), GradLogDensity(), LogDensityImpl(), and SampleImpl().

beta

const Eigen::VectorXd muq::Modeling::Gamma::beta

Definition at line 36 of file Gamma.h.

Referenced by FromMoments(), GradLogDensity(), LogDensityImpl(), and SampleImpl().

logConst

const double muq::Modeling::Gamma::logConst

private

Definition at line 43 of file Gamma.h.

Referenced by ComputeConstant(), and LogDensityImpl().

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

• doxygen_prep/MUQ/Modeling/Distributions/Gamma.h
• doxygen_prep/modules/Modeling/src/Distributions/Gamma.cpp