Set of nonparametric processes (Gaussian process, Student's t process, etc.) for surrogate modelling. More...

Classes
class	bayesopt::ConditionalBayesProcess
	Empirical Bayesian NonParametric process. More...

class	bayesopt::Dataset
	Dataset model to deal with the vector (real) based datasets. More...

class	bayesopt::GaussianProcess
	Standard zero mean gaussian process with noisy observations. More...

class	bayesopt::HierarchicalGaussianProcess
	Virtual class for hierarchical Gaussian processes. More...

class	bayesopt::GaussianProcessML
	Gaussian process with ML parameters. More...

class	bayesopt::GaussianProcessNormal
	Gaussian process with normal prior on the parameters. More...

class	bayesopt::KernelRegressor
	Abstract class to implement non-parametric processes. More...

class	bayesopt::NonParametricProcess
	Abstract class to implement Bayesian regressors. More...

class	bayesopt::StudentTProcessJeffreys
	Student T process with Jeffreys prior. More...

class	bayesopt::StudentTProcessNIG
	Student's t process with Normal Inverse-Gamma hyperprior on mean and snigal variance parameters. More...

Functions
double	bayesopt::ConditionalBayesProcess::evaluate (const vectord &x)
	Computes the score (eg:likelihood) of the kernel parameters. More...

void	bayesopt::Dataset::addSample (const vectord &x, double y)

double	bayesopt::Dataset::getSampleY (size_t index) const

vectord	bayesopt::Dataset::getSampleX (size_t index) const

double	bayesopt::Dataset::getLastSampleY () const

vectord	bayesopt::Dataset::getLastSampleX () const

vectord	bayesopt::Dataset::getPointAtMinimum () const

double	bayesopt::Dataset::getValueAtMinimum () const

size_t	bayesopt::Dataset::getNSamples () const

void	bayesopt::Dataset::updateMinMax (size_t i)

void	bayesopt::KernelRegressor::fitSurrogateModel ()
	Computes the initial surrogate model and updates the kernel parameters estimation. More...

size_t	bayesopt::KernelRegressor::nHyperParameters ()

vectord	bayesopt::KernelRegressor::getHyperParameters ()

void	bayesopt::KernelRegressor::setHyperParameters (const vectord &theta)

void	bayesopt::KernelRegressor::computeCorrMatrix (matrixd &corrMatrix)
	Computes the Correlation (Kernel or Gram) matrix.

matrixd	bayesopt::KernelRegressor::computeCorrMatrix ()
	Computes the Correlation (Kernel or Gram) matrix.

vectord	bayesopt::KernelRegressor::computeCrossCorrelation (const vectord &query)

double	bayesopt::KernelRegressor::computeSelfCorrelation (const vectord &query)

void	bayesopt::KernelRegressor::addNewPointToCholesky (const vectord &correlation, double selfcorrelation)
	Adds a new point to the Cholesky decomposition of the Correlation matrix. More...

double	bayesopt::NonParametricProcess::getValueAtMinimum ()

const Dataset *	bayesopt::NonParametricProcess::getData ()

double	bayesopt::NonParametricProcess::getSignalVariance ()