Posterior model of nonparametric processes/criteria based on MCMC samples. More...

#include <posterior_mcmc.hpp>

Inheritance diagram for bayesopt::MCMCModel:

Collaboration diagram for bayesopt::MCMCModel:

Public Types
typedef boost::ptr_vector< NonParametricProcess >	GPVect

typedef boost::ptr_vector< Criteria >	CritVect

Public Member Functions
	MCMCModel (size_t dim, Parameters params, randEngine &eng)
	Constructor (Note: default constructor is private) More...

void	updateHyperParameters ()

void	fitSurrogateModel ()

void	updateSurrogateModel ()

double	evaluateCriteria (const vectord &query)

void	updateCriteria (const vectord &query)

bool	criteriaRequiresComparison ()

void	setFirstCriterium ()

bool	setNextCriterium (const vectord &prevResult)

std::string	getBestCriteria (vectord &best)

ProbabilityDistribution *	getPrediction (const vectord &query)

Public Member Functions inherited from bayesopt::PosteriorModel
	PosteriorModel (size_t dim, Parameters params, randEngine &eng)
	Constructor. More...

virtual	~PosteriorModel ()
	Default destructor.

void	setSamples (const matrixd &x, const vectord &y)

void	setSamples (const matrixd &x)

void	setSamples (const vectord &y)

void	setSample (const vectord &x, double y)

void	addSample (const vectord &x, double y)

double	getValueAtMinimum ()

vectord	getPointAtMinimum ()

void	plotDataset (TLogLevel level)

const Dataset *	getData ()

Private Member Functions
void	setSurrogateModel (randEngine &eng)

void	setCriteria (randEngine &eng)

	MCMCModel (MCMCModel &copy)

Private Attributes
size_t	nParticles

GPVect	mGP
	Pointer to surrogate model.

CritVect	mCrit
	Metacriteria model.

boost::scoped_ptr< MCMCSampler >	kSampler

Additional Inherited Members
Static Public Member Functions inherited from bayesopt::PosteriorModel
static PosteriorModel *	create (size_t dim, Parameters params, randEngine &eng)

Protected Attributes inherited from bayesopt::PosteriorModel
Parameters	mParameters
	Configuration parameters.

size_t	mDims
	Number of dimensions.

Dataset	mData
	Dataset (x-> inputs, y-> labels/output)

MeanModel	mMean

Detailed Description

Posterior model of nonparametric processes/criteria based on MCMC samples.

For computational reasons we store a copy of each conditional models with the corresponding particle generated by MCMC. That is to avoid costly operations like matrix inversions for every kernel parameter in a GP prediction. Thus, we assume that the number of particles is not very large.

Definition at line 47 of file posterior_mcmc.hpp.

Constructor & Destructor Documentation

◆ MCMCModel()

bayesopt::MCMCModel::MCMCModel	(	size_t	dim,
		Parameters	params,
		randEngine &	eng
	)

Constructor (Note: default constructor is private)

Parameters

dim	number of input dimensions
params	configuration parameters (see parameters.hpp)
eng	random number generation engine (boost)

Definition at line 27 of file posterior_mcmc.cpp.

References mGP.

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

Public Types

Public Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ MCMCModel()