hops::Model Class Reference

#include <Model.hpp>

Inheritance diagram for hops::Model:

Collaboration diagram for hops::Model:

Public Member Functions
virtual	~Model ()=default
virtual MatrixType::Scalar	computeNegativeLogLikelihood (const VectorType &x) const =0
	Evaluates the negative log likelihood for input x. More...
virtual std::optional< VectorType >	computeLogLikelihoodGradient (const VectorType &x) const
virtual std::optional< MatrixType >	computeExpectedFisherInformation (const VectorType &) const
virtual std::optional< std::vector< std::string > >	getParameterNames () const

Constructor & Destructor Documentation

~Model()

virtual hops::Model::~Model ( )

virtualdefault

Member Function Documentation

computeExpectedFisherInformation()

virtual std::optional<MatrixType> hops::Model::computeExpectedFisherInformation ( const VectorType &  ) const

inlinevirtual

Reimplemented in hops::Mixture, hops::Rosenbrock, hops::DegenerateMultivariateGaussian, and hops::MultivariateGaussian.

computeLogLikelihoodGradient()

virtual std::optional<VectorType> hops::Model::computeLogLikelihoodGradient ( const VectorType &  x ) const

inlinevirtual

Reimplemented in hops::Mixture, hops::Rosenbrock, hops::DegenerateMultivariateGaussian, and hops::MultivariateGaussian.

computeNegativeLogLikelihood()

virtual MatrixType::Scalar hops::Model::computeNegativeLogLikelihood ( const VectorType &  x ) const

pure virtual

Evaluates the negative log likelihood for input x.

Parameters

x

Returns

Implemented in hops::Rosenbrock, hops::Mixture, hops::DegenerateMultivariateGaussian, and hops::MultivariateGaussian.

getParameterNames()

virtual std::optional<std::vector<std::string> > hops::Model::getParameterNames ( ) const

inlinevirtual

Reimplemented in hops::ReversibleJumpProposal< MarkovChainImpl, Model >.

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

• Model.hpp