#include <Distribution.h>

Inheritance diagram for muq::Modeling::Distribution:

Detailed Description

Definition at line 18 of file Distribution.h.

Public Member Functions
	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)

virtual Eigen::VectorXd	GradLogDensity (unsigned int wrt, ref_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, ref_vector< Eigen::VectorXd > const &input, Eigen::VectorXd const &vec)

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...

Public Attributes
const unsigned int	varSize

const Eigen::VectorXi	hyperSizes

Friends
class	Density

class	RandomVariable

Constructor & Destructor Documentation

◆ Distribution()

muq::Modeling::Distribution::Distribution	(	unsigned int	varSizeIn,
		Eigen::VectorXi const &	hyperSizesIn = `Eigen::VectorXi()`
	)

inline

Definition at line 23 of file Distribution.h.

◆ ~Distribution()

virtual muq::Modeling::Distribution::~Distribution ( )

virtualdefault

Member Function Documentation

◆ ApplyLogDensityHessian() [1/2]

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

virtual

Reimplemented in muq::Modeling::Gamma.

Definition at line 76 of file Distribution.cpp.

References ApplyLogDensityHessianImpl(), hyperSizes, and varSize.

Referenced by muq::Modeling::DensityBase::ApplyHessianImpl(), and ApplyLogDensityHessian().

◆ ApplyLogDensityHessian() [2/2]

virtual Eigen::VectorXd muq::Modeling::Distribution::ApplyLogDensityHessian	(	unsigned int const	inWrt1,
		unsigned int const	inWrt2,
		std::vector< Eigen::VectorXd > const &	input,
		Eigen::VectorXd const &	vec
	)

inlinevirtual

Definition at line 56 of file Distribution.h.

References ApplyLogDensityHessian(), and ToRefVector().

◆ ApplyLogDensityHessianImpl()

Eigen::VectorXd Distribution::ApplyLogDensityHessianImpl	(	unsigned int	wrt1,
		unsigned int	wrt2,
		ref_vector< Eigen::VectorXd > const &	inputs,
		Eigen::VectorXd const &	vec
	)

protectedvirtual

Reimplemented in muq::Modeling::GaussianBase, muq::Modeling::DensityProduct, and muq::Modeling::Density.

Definition at line 93 of file Distribution.cpp.

References nlohmann::detail::dtoa_impl::e, and GradLogDensity().

Referenced by ApplyLogDensityHessian().

◆ AsDensity()

std::shared_ptr< Density > Distribution::AsDensity ( )

Returns a density built from this distribution.

The distribution class allows users to both evaluate the density corresponding to a probability distribution and draw a sample of the corresponding random variable. Both of these actions are supported through the Evaluate function, where the first input to Evaluate specifies what action to perform: evaluate the density or sample the RV. However, sometimes we only want to focus on the density part of the distribution. This function returns a Density object that only supports evaluting the log density and does not require the extra input specifying what type of action to perform.

For example,

std::shared_ptr<Distribution> dist = std::make_shared<Gaussian>(mu,cov);
 
Eigen::VectorXd x;
// ... initialize the point x
 
// To obtain the density through Evaluate, we need to call
boost::any densVal = dist->Evaluate(Distribution::Mode::EvaluateLogDensity, x);
 
// With Density however, we don't need to add the additional flag
boost::any densVal2 = dist->AsDensity()->Evaluate(x);
 
// or, analogously
std::shared_ptr<Density> dens = dist->AsDensity();
boost::any densVal3 = dens->Evaluate(x);

Definition at line 9 of file Distribution.cpp.

◆ AsVariable()

std::shared_ptr< RandomVariable > Distribution::AsVariable ( )

Returns a random variable built from this distribution.

The distribution class allows users to both evaluate the density corresponding to a probability distribution and draw a sample of the corresponding random variable. Both of these actions are supported through the Evaluate function, where the first input to Evaluate specifies what action to perform: evaluate the density or sample the RV. However, sometimes we only want to focus on the random variable part (i.e., the "Sample" function) of the distribution. This function returns a RandomVariable object that only supports drawing realizations of the distribution, not evaluting the density. Because the RandomVariable only does one thing, it does not require the extra input specifying what type of action to perform.

For example,

std::shared_ptr<Distribution> dist = std::make_shared<Gaussian>(mu,cov);
 
Eigen::VectorXd x;
// ... initialize the point x
 
// To obtain a sample, we need to call
boost::any sample = dist->Evaluate(Distribution::Mode::SampleDistribution, x);
 
// With the RandomVariable class however, we don't need to add the additional flag
boost::any sample2 = dist->AsVariable()->Evaluate(x);
 
// or, analogously
std::shared_ptr<RandomVariable> rv = dist->AsVariable();
boost::any sample3 = rv->Evaluate(x);

Definition at line 14 of file Distribution.cpp.

◆ GradLogDensity() [1/4]

template<typename... Args>

Eigen::VectorXd muq::Modeling::Distribution::GradLogDensity	(	unsigned int	wrt,
		Args...	args
	)

inline

Definition at line 44 of file Distribution.h.

References GradLogDensity().

◆ GradLogDensity() [2/4]

template<typename... Args>

Eigen::VectorXd muq::Modeling::Distribution::GradLogDensity	(	unsigned int	wrt,
		ref_vector< Eigen::VectorXd > &	inputs,
		Eigen::VectorXd const &	in,
		Args...	args
	)

inlineprivate

Definition at line 180 of file Distribution.h.

References GradLogDensity().

◆ GradLogDensity() [3/4]

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

virtual

Reimplemented in muq::Modeling::Gamma.

Definition at line 44 of file Distribution.cpp.

References GradLogDensityImpl().

◆ GradLogDensity() [4/4]

virtual Eigen::VectorXd muq::Modeling::Distribution::GradLogDensity	(	unsigned int	wrt,
		std::vector< Eigen::VectorXd > const &	inputs
	)

inlinevirtual

Definition at line 40 of file Distribution.h.

References GradLogDensity(), and ToRefVector().

Referenced by muq::Modeling::DensityBase::ApplyHessianImpl(), muq::Modeling::DensityBase::ApplyJacobianImpl(), ApplyLogDensityHessianImpl(), muq::Modeling::DensityBase::GradientImpl(), GradLogDensity(), and muq::Modeling::DensityBase::JacobianImpl().

◆ GradLogDensityImpl()

Eigen::VectorXd Distribution::GradLogDensityImpl	(	unsigned int	wrt,
		ref_vector< Eigen::VectorXd > const &	inputs
	)

protectedvirtual

Reimplemented in muq::Modeling::RandomVariable, muq::Modeling::MixtureDistribution, muq::Modeling::GaussianBase, muq::Modeling::DensityProduct, and muq::Modeling::Density.

Definition at line 50 of file Distribution.cpp.

References nlohmann::detail::dtoa_impl::e, and LogDensity().

Referenced by GradLogDensity().

◆ LogDensity() [1/2]

double Distribution::LogDensity ( ref_vector< Eigen::VectorXd > const & inputs )

virtual

Evaluate the log-density.

If known, the log-density should be implemented by a child in the LogDensityImpl class.

Parameters

[in] inputs the vector of inputs to the log-density

Returns: The log density

Definition at line 31 of file Distribution.cpp.

References LogDensityImpl().

Referenced by muq::Modeling::DensityBase::EvaluateImpl(), GradLogDensityImpl(), muq::SamplingAlgorithms::InverseGammaProposal::LogDensity(), and muq::SamplingAlgorithms::SMMALAProposal::LogDensity().

◆ LogDensity() [2/2]

virtual double muq::Modeling::Distribution::LogDensity ( std::vector< Eigen::VectorXd > const & inputs )

inlinevirtual

Definition at line 36 of file Distribution.h.

References LogDensity(), and ToRefVector().

Referenced by LogDensity().

◆ LogDensityImpl()

virtual double muq::Modeling::Distribution::LogDensityImpl ( ref_vector< Eigen::VectorXd > const & inputs )

inlineprotectedvirtual

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 in muq::Modeling::UniformBox, muq::Modeling::RandomVariable, muq::Modeling::PyDistribution, muq::Modeling::MixtureDistribution, muq::Modeling::InverseGamma, muq::Modeling::GaussianBase, muq::Modeling::Gamma, muq::Modeling::DensityProduct, and muq::Modeling::Density.

Definition at line 156 of file Distribution.h.

Referenced by LogDensity().

◆ Sample() [1/3]

Eigen::VectorXd Distribution::Sample ( )

Sample the distribution with no inputs.

Allows the user to call Sample without any inputs

Returns: A sample

Definition at line 40 of file Distribution.cpp.

◆ Sample() [2/3]

Eigen::VectorXd Distribution::Sample ( ref_vector< Eigen::VectorXd > const & inputs )

Sample the distribution.

Calls SampleImpl, the default behavior is to return boost::none

Parameters

[in] inputs the vector of inputs to the log-density

Returns: A sample

Definition at line 36 of file Distribution.cpp.

References SampleImpl().

Referenced by muq::SamplingAlgorithms::InverseGammaProposal::Sample(), and muq::SamplingAlgorithms::SMMALAProposal::Sample().

◆ Sample() [3/3]

Eigen::VectorXd muq::Modeling::Distribution::Sample ( std::vector< Eigen::VectorXd > const & inputs )

inline

Definition at line 69 of file Distribution.h.

References Sample(), and ToRefVector().

Referenced by Sample().

◆ SampleImpl()

virtual Eigen::VectorXd muq::Modeling::Distribution::SampleImpl ( ref_vector< Eigen::VectorXd > const & inputs )

inlineprotectedvirtual

Sample the distribution.

Should be overwritten by a child. The default behavior is to return boost::none

Reimplemented in muq::Modeling::UniformBox, muq::Modeling::RandomVariable, muq::Modeling::PyDistribution, muq::Modeling::MixtureDistribution, muq::Modeling::InverseGamma, muq::Modeling::GaussianBase, muq::Modeling::Gamma, and muq::Modeling::Density.

Definition at line 170 of file Distribution.h.

Referenced by Sample().

◆ ToRefVector()

ref_vector< const Eigen::VectorXd > Distribution::ToRefVector ( std::vector< Eigen::VectorXd > const & anyVec ) const

protected

Definition at line 19 of file Distribution.cpp.

Referenced by ApplyLogDensityHessian(), GradLogDensity(), LogDensity(), and Sample().

◆ VARIADIC_TO_REFVECTOR() [1/2]

muq::Modeling::Distribution::VARIADIC_TO_REFVECTOR	(	LogDensity	,
		Eigen::VectorXd	,
		double
	)

◆ VARIADIC_TO_REFVECTOR() [2/2]

muq::Modeling::Distribution::VARIADIC_TO_REFVECTOR	(	Sample	,
		Eigen::VectorXd	,
		Eigen::VectorXd
	)

Friends And Related Function Documentation

◆ Density

friend class Density

friend

Definition at line 20 of file Distribution.h.

◆ RandomVariable

friend class RandomVariable

friend

Definition at line 21 of file Distribution.h.

Member Data Documentation

◆ hyperSizes

const Eigen::VectorXi muq::Modeling::Distribution::hyperSizes

Definition at line 146 of file Distribution.h.

Referenced by ApplyLogDensityHessian(), muq::Modeling::MixtureDistribution::GradLogDensityImpl(), and muq::Modeling::MixtureDistribution::MixtureDistribution().

◆ varSize

const unsigned int muq::Modeling::Distribution::varSize

Definition at line 145 of file Distribution.h.

Referenced by ApplyLogDensityHessian(), muq::Modeling::MixtureDistribution::GradLogDensityImpl(), and muq::Modeling::MixtureDistribution::MixtureDistribution().

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

doxygen_prep/MUQ/Modeling/Distributions/Distribution.h
doxygen_prep/modules/Modeling/src/Distributions/Distribution.cpp

Detailed Description

Public Member Functions

Public Attributes

Friends

Constructor & Destructor Documentation

◆ Distribution()

◆ ~Distribution()

Member Function Documentation

◆ ApplyLogDensityHessian() [1/2]

◆ ApplyLogDensityHessian() [2/2]

◆ ApplyLogDensityHessianImpl()

◆ AsDensity()

◆ AsVariable()

◆ GradLogDensity() [1/4]

◆ GradLogDensity() [2/4]

◆ GradLogDensity() [3/4]

◆ GradLogDensity() [4/4]

◆ GradLogDensityImpl()

◆ LogDensity() [1/2]

◆ LogDensity() [2/2]

◆ LogDensityImpl()

◆ Sample() [1/3]

◆ Sample() [2/3]

◆ Sample() [3/3]

◆ SampleImpl()

◆ ToRefVector()

◆ VARIADIC_TO_REFVECTOR() [1/2]

◆ VARIADIC_TO_REFVECTOR() [2/2]

Friends And Related Function Documentation

◆ Density

◆ RandomVariable

Member Data Documentation

◆ hyperSizes

◆ varSize