Derived class for gaussian-marginal likelihood with discrete parameter. More...

#include <post.h>

Inheritance diagram for Lik_GausMargD:

Public Member Functions
	Lik_GausMargD ()
	Constructor.

	~Lik_GausMargD ()
	Destructor.

double	evalLogLik (Array1D< double > &m)
	Evaluate log-likelihood.

Public Member Functions inherited from Post
	Post ()
	Constructor.

	~Post ()
	Destructor.

void	setData (Array2D< double > &xdata, Array2D< double > &ydata)
	Set the x- and y-data.

void	setData (Array2D< double > &xdata, Array1D< Array1D< double > > &ydata)
	Set the x- and y-data, for the case when each datapoint have different number of measurements.

void	setDataNoise (Array1D< double > &sigma)
	Set the magnitude of data noise.

void	inferDataNoise ()
	Indicate inference of data noise stdev.

void	inferLogDataNoise ()
	Indicate inference of log of data noise stdev.

Array1D< double >	dataSigma (double m_last)
	Get data noise, whether inferred or fixed.

void	setModel (Array1D< Array2D< double >()(Array2D< double > &, Array2D< double > &, Array2D< double > &, void ) > forwardFuncs, Array2D< double > &fixindnom, void *funcInfo)
	Set a pointer to the forward model f(p,x)

void	setModelRVinput (int pdim, int order, Array1D< int > &rndInd, string pdfType, string pcType)
	Set model input parameters' randomization scheme.

int	getChainDim ()
	Get the dimensionailty of the posterior function.

void	setPrior (string priorType, double priora, double priorb)
	Set the prior type and its parameters.

double	evalLogPrior (Array1D< double > &m)
	Evaluate log-prior.

Array2D< double >	getParamPCcf (Array1D< double > &m)
	Extract parameter PC coefficients from a posterior input.

Array2D< double >	samParam (Array1D< double > &m, int ns)
	Sample model parameters given posterior input.

void	momParam (Array1D< double > &m, Array1D< double > &parMean, Array1D< double > &parVar, bool covFlag, Array2D< double > &parCov)
	Get moments of parameters given posterior input.

Array2D< double >	samForwardFcn (Array2D< double >(forwardFunc)(Array2D< double > &, Array2D< double > &, Array2D< double > &, void ), Array1D< double > &m, Array2D< double > &xgrid, int ns)
	Sample forward function at a given grid for given posterior input.

void	momForwardFcn (Array2D< double >(forwardFunc)(Array2D< double > &, Array2D< double > &, Array2D< double > &, void ), Array1D< double > &m, Array2D< double > &xgrid, Array1D< double > &fcnMean, Array1D< double > &fcnVar, bool covflag, Array2D< double > &fcnCov)
	Get moments of forward function at a given grid for given posterior input.

void	momForwardFcn (Array1D< double > &m, Array2D< double > &xgrid, Array1D< double > &fcnMean, Array1D< double > &fcnVar, bool covflag, Array2D< double > &fcnCov)
	Get moments of composite forward function at a given grid for given posterior input.

Additional Inherited Members
Protected Attributes inherited from Post
Array2D< double >	xData_
	xdata

Array1D< Array1D< double > >	yData_
	ydata

Array1D< double >	yDatam_
	ydata averaged per measurement

int	nData_
	Number of data points.

Array1D< int >	nEachs_
	Number of samples at each input.

int	xDim_
	Dimensionality of x-space.

int	pDim_
	Dimensionality of parameter space (p-space)

int	chDim_
	Dimensionality of posterior input.

bool	inferDataNoise_
	Flag for data noise inference.

bool	dataNoiseLogFlag_
	Flag to check if data noise logarithm is used.

Array1D< double >	dataNoiseSig_
	Data noise stdev.

Array1D< Array2D< double >()(Array2D< double > &, Array2D< double > &, Array2D< double > &, void ) >	forwardFcns_
	Pointer to the forward function f(p,x)

void *	funcinfo_
	Auxiliary information for function evaluation.

int	extraInferredParams_
	Number of extra inferred parameters, such as data noise or Koh variance.

int	ncat_
	Number of categories.

Mrv *	Mrv_
	Pointer to a multivariate PC RV object.

Array1D< int >	rndInd_
	Indices of randomized inputs.

Array2D< double >	fixIndNom_
	Indices and nominal values for fixed inputs.

Array1D< double >	lower_
	Lower and upper bounds on parameters.

Array1D< double >	upper_

string	pdfType_
	Input parameter PDF type.

string	rvpcType_
	PC type parameter for the r.v.

string	priorType_
	Prior type.

double	priora_
	Prior parameter #1.

double	priorb_
	Prior parameter #2.

Detailed Description

Derived class for gaussian-marginal likelihood with discrete parameter.

Constructor & Destructor Documentation

◆ Lik_GausMargD()

Lik_GausMargD::Lik_GausMargD ( )

inline

Constructor.

◆ ~Lik_GausMargD()

Lik_GausMargD::~Lik_GausMargD ( )

inline

Destructor.

Member Function Documentation

◆ evalLogLik()

double Lik_GausMargD::evalLogLik ( Array1D< double > & m )

virtual

Evaluate log-likelihood.

Reimplemented from Post.

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

post.h