A class for handling Bayesian joint PDFs. More...

#include <BayesianJointPdf.h>

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

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

Public Member Functions
Constructor/Destructor methods
	BayesianJointPdf (const char *prefix, const BaseJointPdf< V, M > &priorDensity, const BaseScalarFunction< V, M > &likelihoodFunction, double likelihoodExponent, const VectorSet< V, M > &intersectionDomain)
	Default constructor. More...

	~BayesianJointPdf ()
	Destructor. More...

Math methods
double	actualValue (const V &domainVector, const V domainDirection, V gradVector, M hessianMatrix, V hessianEffect) const
	Actual value of the PDF (scalar function). More...

double	lnValue (const V &domainVector, const V domainDirection, V gradVector, M hessianMatrix, V hessianEffect) const
	Computes the logarithm of the value of the function. More...

double	computeLogOfNormalizationFactor (unsigned int numSamples, bool updateFactorInternally) const
	TODO: Computes the logarithm of the normalization factor. More...

void	setNormalizationStyle (unsigned int value) const
	Sets a value to be used in the normalization style of the prior density PDF (ie, protected attribute m_priorDensity). More...

double	lastComputedLogPrior () const
	Returns the logarithm of the last computed Prior value. Access to protected attribute m_lastComputedLogPrior. More...

double	lastComputedLogLikelihood () const
	Returns the logarithm of the last computed likelihood value. Access to protected attribute m_lastComputedLogLikelihood. More...

Public Member Functions inherited from QUESO::BaseJointPdf< V, M >
	BaseJointPdf (const char *prefix, const VectorSet< V, M > &domainSet)
	Default constructor. More...

virtual	~BaseJointPdf ()
	Destructor. More...

void	setLogOfNormalizationFactor (double value) const
	Sets a logarithmic value to be used in the normalization factor (stored in the protected attribute m_normalizationStyle.) More...

Public Member Functions inherited from QUESO::BaseScalarFunction< V, M >
	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...

Protected Attributes
const BaseJointPdf< V, M > &	m_priorDensity

const BaseScalarFunction< V, M > &	m_likelihoodFunction

double	m_likelihoodExponent

double	m_lastComputedLogPrior

double	m_lastComputedLogLikelihood

V	m_tmpVector1

V	m_tmpVector2

M *	m_tmpMatrix

Protected Attributes inherited from QUESO::BaseJointPdf< V, M >
unsigned int	m_normalizationStyle

double	m_logOfNormalizationFactor

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...

Additional Inherited Members
Protected Member Functions inherited from QUESO::BaseJointPdf< V, M >
double	commonComputeLogOfNormalizationFactor (unsigned int numSamples, bool updateFactorInternally) const
	Common method (to the derived classes) to compute the logarithm of the normalization factor. More...

Detailed Description

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

A class for handling Bayesian joint PDFs.

This class allows the mathematical definition of a Bayesian Joint PDF.

Definition at line 49 of file BayesianJointPdf.h.

Constructor & Destructor Documentation

template<class V, class M>

QUESO::BayesianJointPdf< V, M >::BayesianJointPdf	(	const char *	prefix,
		const BaseJointPdf< V, M > &	priorDensity,
		const BaseScalarFunction< V, M > &	likelihoodFunction,
		double	likelihoodExponent,
		const VectorSet< V, M > &	intersectionDomain
	)

Default constructor.

Instantiates an object of this class given a prefix and a scalar function. The domain of the scalar function is assigned to the protected attribute m_domainSet, and the scalar fiction is also itself copied to the protected attribute m_scalarFunction.

Definition at line 33 of file BayesianJointPdf.C.

   :
   BaseJointPdf<V,M>(((std::string)(prefix)+"bay").c_str(),intersectionDomain),
   m_priorDensity             (priorDensity),
   m_likelihoodFunction       (likelihoodFunction),
   m_likelihoodExponent       (likelihoodExponent),
   m_lastComputedLogPrior     (0.),
   m_lastComputedLogLikelihood(0.),
   m_tmpVector1               (m_domainSet.vectorSpace().zeroVector()),
   m_tmpVector2               (m_domainSet.vectorSpace().zeroVector()),
   m_tmpMatrix                (m_domainSet.vectorSpace().newMatrix())
 {
 }

template<class V , class M >

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

Destructor.

Definition at line 53 of file BayesianJointPdf.C.

 {
   delete m_tmpMatrix;
 }

Member Function Documentation

template<class V, class M>

double QUESO::BayesianJointPdf< V, M >::actualValue	(	const V &	domainVector,
		const V *	domainDirection,
		V *	gradVector,
		M *	hessianMatrix,
		V *	hessianEffect
	)		const

virtual

Actual value of the PDF (scalar function).

If the exponent of the likelihood function (likelihoodExponent) is zero, i.e. the likelihood is constant and unitary, then the actual value is the value of the prior PDF; otherwise, the actual value is scaled (multiplied) by a power of the value of the likelihood function.

Implements QUESO::BaseJointPdf< V, M >.

Definition at line 82 of file BayesianJointPdf.C.

References queso_require_equal_to_msg, and queso_require_msg.

 {
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "Entering BayesianJointPdf<V,M>::actualValue()"
                             << ": domainVector = " << domainVector
                             << std::endl;
   }
 
   queso_require_equal_to_msg(domainVector.sizeLocal(), this->m_domainSet.vectorSpace().dimLocal(), "invalid input");
 
   V* gradVLike = NULL;
   if (gradVector) gradVLike = &m_tmpVector1;
 
   M* hessianMLike = NULL;
   if (hessianMatrix) hessianMLike = m_tmpMatrix;
 
   V* hessianELike = NULL;
   if (hessianEffect) hessianELike = &m_tmpVector2;
 
   double value1 = m_priorDensity.actualValue (domainVector,domainDirection,gradVector,hessianMatrix,hessianEffect);
   double value2 = 1.;
   if (m_likelihoodExponent != 0.) {
     value2 = m_likelihoodFunction.actualValue(domainVector,domainDirection,gradVLike ,hessianMLike ,hessianELike );
   }
 
   queso_require_msg(!(gradVector || hessianMatrix || hessianEffect), "incomplete code for gradVector, hessianMatrix and hessianEffect calculations");
 
   double returnValue = value1;
   if (m_likelihoodExponent == 0.) {
     // Do nothing
   }
   else if (m_likelihoodExponent == 1.) {
     returnValue *= value2;
   }
   else {
     returnValue *= pow(value2,m_likelihoodExponent);
   }
   returnValue *= exp(m_logOfNormalizationFactor); // [PDF-02] ???
 
   m_lastComputedLogPrior      = log(value1);
   m_lastComputedLogLikelihood = m_likelihoodExponent*log(value2);
 
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "Leaving BayesianJointPdf<V,M>::actualValue()"
                             << ": domainVector = " << domainVector
                             << ", returnValue = "  << returnValue
                             << std::endl;
   }
 
   return returnValue;
 }

template<class V , class M >

double QUESO::BayesianJointPdf< V, M >::computeLogOfNormalizationFactor	(	unsigned int	numSamples,
		bool	updateFactorInternally
	)		const

virtual

TODO: Computes the logarithm of the normalization factor.

Todo:: : implement me!

Implements QUESO::BaseJointPdf< V, M >.

Definition at line 260 of file BayesianJointPdf.C.

References queso_error_msg.

 {
   double value = 0.;
 
   double volume = m_domainSet.volume();
   if (((boost::math::isnan)(volume)) ||
       (volume == -INFINITY         ) ||
       (volume ==  INFINITY         ) ||
       (volume <= 0.                )) {
     // Do nothing
   }
   else {
     queso_error_msg("incomplete code for computeLogOfNormalizationFactor()");
   }
 
   return value;
 }

template<class V , class M >

double QUESO::BayesianJointPdf< V, M >::lastComputedLogLikelihood ( ) const

Returns the logarithm of the last computed likelihood value. Access to protected attribute m_lastComputedLogLikelihood.

Definition at line 75 of file BayesianJointPdf.C.

 {
   return m_lastComputedLogLikelihood;
 }

template<class V , class M >

double QUESO::BayesianJointPdf< V, M >::lastComputedLogPrior ( ) const

Returns the logarithm of the last computed Prior value. Access to protected attribute m_lastComputedLogPrior.

Definition at line 68 of file BayesianJointPdf.C.

 {
   return m_lastComputedLogPrior;
 }

template<class V, class M>

double QUESO::BayesianJointPdf< V, M >::lnValue	(	const V &	domainVector,
		const V *	domainDirection,
		V *	gradVector,
		M *	hessianMatrix,
		V *	hessianEffect
	)		const

virtual

Computes the logarithm of the value of the function.

Analogously to the method actualValue(), if the exponent of the likelihood function (likelihoodExponent) is zero then the Logarithm of the value of the function is the logarithm of the value of the prior PDF; otherwise, the value is scaled (added) by a power of the value of the likelihood function.

Implements QUESO::BaseJointPdf< V, M >.

Definition at line 141 of file BayesianJointPdf.C.

 {
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "Entering BayesianJointPdf<V,M>::lnValue()"
                             << ": domainVector = " << domainVector
                             << std::endl;
   }
 
   V* gradVLike = NULL;
   if (gradVector) {
     gradVLike = &m_tmpVector1;
 
     // DM:  We do this because we want to be able to test whether the user
     //      actually filled gradVector, rather than passing a different
     //      instance of V to the likelihood code
     for (unsigned int i = 0; i < m_tmpVector1.sizeLocal(); i++) {
       m_tmpVector1[i] = (*gradVector)[i];
     }
   }
 
   M* hessianMLike = NULL;
   if (hessianMatrix) hessianMLike = m_tmpMatrix;
 
   V* hessianELike = NULL;
   if (hessianEffect) hessianELike = &m_tmpVector2;
 
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                             << ", domainVector = " << domainVector
                             << ": about to call prior()"
                             << std::endl;
   }
 
   double value1 = m_priorDensity.lnValue(domainVector,domainDirection,gradVector,hessianMatrix,hessianEffect);
 
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                             << ", domainVector = " << domainVector
                             << ": lnPrior = " << value1
                             << std::endl;
   }
 
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                             << ", domainVector = " << domainVector
                             << ": about to call likelihood()"
                             << std::endl;
   }
 
   double value2 = 0.;
   if (m_likelihoodExponent != 0.) {
     value2 = m_likelihoodFunction.lnValue(domainVector,domainDirection,gradVLike, hessianMLike, hessianELike );
   }
 
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                             << ", domainVector = " << domainVector
                             << ": value1 = "       << value1
                             << ", value2 = "       << value2
                             << std::endl;
     if (gradVector) {
       *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                               << ", domainVector = " << domainVector
                               << ": gradVector = "   << *gradVector
                               << ", gradVLike = "    << *gradVLike
                               << std::endl;
     }
     if (hessianMatrix) {
       *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                               << ", domainVector = "  << domainVector
                               << ": hessianMatrix = " << *hessianMatrix
                               << ", hessianMLike = "  << *hessianMLike
                               << std::endl;
     }
     if (hessianEffect) {
       *m_env.subDisplayFile() << "In BayesianJointPdf<V,M>::lnValue()"
                               << ", domainVector = "  << domainVector
                               << ": hessianEffect = " << *hessianEffect
                               << ", hessianELike = "  << *hessianELike
                               << std::endl;
     }
   }
 
   if (gradVector   ) *gradVector    += *gradVLike;
   if (hessianMatrix) *hessianMatrix += *hessianMLike;
   if (hessianEffect) *hessianEffect += *hessianELike;
 
   double returnValue = value1;
   if (m_likelihoodExponent == 0.) {
     // Do nothing
   }
   else if (m_likelihoodExponent == 1.) {
     returnValue += value2;
   }
   else {
     returnValue += value2*m_likelihoodExponent;
   } // prudenci 2010/03/05
   returnValue += m_logOfNormalizationFactor; // [PDF-02] ???
 
   m_lastComputedLogPrior      = value1;
   m_lastComputedLogLikelihood = m_likelihoodExponent*value2;
 
   if ((m_env.subDisplayFile()) && (m_env.displayVerbosity() >= 54)) {
     *m_env.subDisplayFile() << "Leaving BayesianJointPdf<V,M>::lnValue()"
                             << ": domainVector = " << domainVector
                             << ", returnValue = "  << returnValue
                             << std::endl;
   }
 
   return returnValue;
 }

template<class V , class M >

void QUESO::BayesianJointPdf< V, M >::setNormalizationStyle ( unsigned int value ) const

virtual

Sets a value to be used in the normalization style of the prior density PDF (ie, protected attribute m_priorDensity).

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

Definition at line 60 of file BayesianJointPdf.C.

 {
   m_priorDensity.setNormalizationStyle(value);
   return;
 }