QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M > Class Template Reference

A class that represents a Gaussian likelihood with full covariance and random coefficient. More...

#include <GaussianLikelihoodFullCovarianceRandomCoefficient.h>

Inheritance diagram for QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >:

Collaboration diagram for QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >:

Public Member Functions
virtual double	lnValue (const V &domainVector) const
	Logarithm of the value of the scalar function. More...
Constructor/Destructor methods.
	GaussianLikelihoodFullCovarianceRandomCoefficient (const char *prefix, const VectorSet< V, M > &domainSet, const V &observations, const M &covariance)
	Default constructor. More...
virtual	~GaussianLikelihoodFullCovarianceRandomCoefficient ()
	Destructor. More...
Public Member Functions inherited from QUESO::LikelihoodBase< V, M >
virtual void	evaluateModel (const V &domainVector, const V *domainDirection, V &modelOutput, V *gradVector, M *hessianMatrix, V *hessianEffect) const
	Deprecated. Evaluates the user's model at the point domainVector. More...
virtual void	evaluateModel (const V &domainVector, V &modelOutput) const
	Evaluates the user's model at the point domainVector. More...
virtual double	actualValue (const V &domainVector, const V *, V *, M *, V *) const
	Actual value of the scalar function. More...
	LikelihoodBase (const char *prefix, const VectorSet< V, M > &domainSet, const V &observations)
	Default constructor. More...
virtual	~LikelihoodBase ()=0
	Destructor, pure to make this class abstract. More...
Public Member Functions inherited from QUESO::BaseScalarFunction< V, M >
void	setFiniteDifferenceStepSize (double fdStepSize)
	Sets the step size for finite differencing gradients. More...
void	setFiniteDifferenceStepSize (unsigned int i, double fdStepSize)
	BaseScalarFunction (const char *prefix, const VectorSet< V, M > &domainSet)
	Default constructor. More...
virtual	~BaseScalarFunction ()
	Destructor. More...
const VectorSet< V, M > &	domainSet () const
	Access to the protected attribute m_domainSet: domain set of the scalar function. More...
virtual double	lnValue (const V &domainVector, const V *domainDirection, V *gradVector, M *hessianMatrix, V *hessianEffect) const
	Logarithm of the value of the scalar function. Deprecated. More...
virtual double	lnValue (const V &domainVector, V &gradVector) const
	Returns the logarithm of the function and its gradient at domainVector. More...
virtual double	lnValue (const V &domainVector, V &gradVector, const V &domainDirection, V &hessianEffect) const

Private Attributes
const M &	m_covariance

Additional Inherited Members
Protected Attributes inherited from QUESO::LikelihoodBase< V, M >
const V &	m_observations
Protected Attributes inherited from QUESO::BaseScalarFunction< V, M >
const BaseEnvironment &	m_env
std::string	m_prefix
const VectorSet< V, M > &	m_domainSet
	Domain set of the scalar function. More...

Detailed Description

template<class V = GslVector, class M = GslMatrix>
class QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >

A class that represents a Gaussian likelihood with full covariance and random coefficient.

The random coefficient is a scalar that pre-multiplies the covariance matrix. This is treated as a hyperparameter to be inferred during the sampling procedure.

Definition at line 48 of file GaussianLikelihoodFullCovarianceRandomCoefficient.h.

Constructor & Destructor Documentation

template<class V , class M >

QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >::GaussianLikelihoodFullCovarianceRandomCoefficient	(	const char *	prefix,
		const VectorSet< V, M > &	domainSet,
		const V &	observations,
		const M &	covariance
	)

Default constructor.

Instantiates a Gaussian likelihood function, given a prefix, its domain, a set of observations and a full covariance matrix. The full covariance matrix is stored as a matrix in the covariance parameter.

The parameter covarianceCoefficient is a multiplying factor of covaraince and is treated as a random variable (i.e. it is solved for in a statistical inversion).

Definition at line 34 of file GaussianLikelihoodFullCovarianceRandomCoefficient.C.

  : LikelihoodBase<V, M>(prefix, domainSet, observations),
    m_covariance(covariance)
{
  if (covariance.numRowsLocal() != observations.sizeLocal()) {
    queso_error_msg("Covariance matrix not same size as observation vector");
  }
}

template<class V , class M >

QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >::~GaussianLikelihoodFullCovarianceRandomCoefficient ( )

virtual

Destructor.

Definition at line 46 of file GaussianLikelihoodFullCovarianceRandomCoefficient.C.

{
}

Member Function Documentation

template<class V , class M >

double QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >::lnValue ( const V &  domainVector ) const

virtual

Logarithm of the value of the scalar function.

Reimplemented from QUESO::BaseScalarFunction< V, M >.

Definition at line 52 of file GaussianLikelihoodFullCovarianceRandomCoefficient.C.

{
  V modelOutput(this->m_observations, 0, 0);  // At least it's not a copy
  V weightedMisfit(this->m_observations, 0, 0);  // At least it's not a copy

  this->evaluateModel(domainVector, modelOutput);

  // Compute misfit G(x) - y
  modelOutput -= this->m_observations;

  // Solve \Sigma u = G(x) - y for u
  this->m_covariance.invertMultiply(modelOutput, weightedMisfit);

  // Compute (G(x) - y)^T \Sigma^{-1} (G(x) - y)
  modelOutput *= weightedMisfit;

  // This is square of 2-norm
  double norm2_squared = modelOutput.sumOfComponents();

  // Get the determinant of the covariance matrix |\Sigma|
  double deter_cov = this->m_covariance.determinant();

  deter_cov = std::sqrt(deter_cov);

  // Set the right hyperparameter coefficient
  // The last element of domainVector is the multiplicative coefficient of the
  // covariance matrix
  double cov_coeff = domainVector[domainVector.sizeLocal()-1];
  cov_coeff = std::pow(std::sqrt(cov_coeff), this->m_observations.sizeLocal());

  return -0.5 * norm2_squared / cov_coeff - std::log(cov_coeff * deter_cov);
}

Member Data Documentation

template<class V = GslVector, class M = GslMatrix>

const M& QUESO::GaussianLikelihoodFullCovarianceRandomCoefficient< V, M >::m_covariance

private

Definition at line 74 of file GaussianLikelihoodFullCovarianceRandomCoefficient.h.

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

• src/stats/inc/GaussianLikelihoodFullCovarianceRandomCoefficient.h
• src/stats/src/GaussianLikelihoodFullCovarianceRandomCoefficient.C