This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 123456]

▼Cbayesopt::BayesOptBase	Abstract module for Bayesian optimization
►Cbayesopt::ContinuousModel	Bayesian optimization for functions in continuous input spaces
CBraninNormalized
CCContinuousModel	Version of ContinuousModel for the C wrapper
CExampleCamelback
CExampleHartmann6
CExampleOneD
CExampleQuadratic
CSystemCallsBranin
CXMLCallsBranin
►Cbayesopt::DiscreteModel	Bayesian optimization for functions in discrete spaces
CCDiscreteModel	Version of DiscreteModel for the C wrapper
CExampleDisc
Cbayesoptmodule.BayesOptCategorical	Python Module for BayesOptCategorical
Cbayesoptmodule.BayesOptDiscrete	Python Module for BayesOptDiscrete
Cbopt_params	Configuration parameters
Cbayesopt::BOptState	Class that represents the state of an optimization
Cbayesopt::utils::BoundingBox< V >	Defines a bounding box or axis-alligned bound constraints
▼Cbayesopt::Criteria	Abstract interface for criteria functors
Cbayesopt::AnnealedExpectedImprovement	Expected improvement criterion using Schonlau annealing. [Schonlau98]
Cbayesopt::AnnealedLowerConfindenceBound	Lower (upper) confidence bound using Srinivas annealing [Srinivas10]
Cbayesopt::BiasedExpectedImprovement	Expected improvement criterion modification by Lizotte
►Cbayesopt::CombinedCriteria	Abstract class for combined criteria functions
►Cbayesopt::GP_Hedge	GP_Hedge model as describen in Hoffman et al
Cbayesopt::GP_Hedge_Random	Modification of the GP_Hedge algorithm where the bandit gains are random outcomes (like Thompson sampling)
Cbayesopt::ProdCriteria	Product of criterion functions
Cbayesopt::SumCriteria	Wrapper class for linear combination of criterion functions
Cbayesopt::ExpectedImprovement	Expected improvement criterion by Mockus [Mockus78]
Cbayesopt::ExpectedReturn	Expected return criterion
Cbayesopt::GreedyAOptimality	Greedy A-Optimality criterion
Cbayesopt::InputDistance	Distance in input space
Cbayesopt::LowerConfidenceBound	Lower (upper) confidence bound criterion by [Cox and John, 1992]
Cbayesopt::MutualInformation	Mutual Information bound criterion b [Contal et al., 2014]
Cbayesopt::OptimisticSampling	Optimistic sampling
Cbayesopt::ProbabilityOfImprovement	Probability of improvement criterion based on (Kushner)
Cbayesopt::ThompsonSampling	Thompson sampling. Picks a random sample of the surrogate model
Cbayesopt::CriteriaFactory	Factory model for criterion functions This factory is based on the libgp library by Manuel Blum https://bitbucket.org/mblum/libgp which follows the squeme of GPML by Rasmussen and Nickisch http://www.gaussianprocess.org/gpml/code/matlab/doc/
Cbayesopt::utils::CUnique	Simple class to generate sequences of unique numbers
Cbayesopt::Dataset	Dataset model to deal with the vector (real) based datasets
Cbayesopt::utils::FileParser
▼Cbayesopt::Kernel	Interface for kernel functors
►Cbayesopt::AtomicKernel	Abstract class for an atomic kernel
►Cbayesopt::ARDkernel	Abstract class for anisotropic kernel functors using ARD (Automatic Relevance Determination)
Cbayesopt::MaternARD1	Matern ARD kernel of 1st order
Cbayesopt::MaternARD3	Matern ARD kernel of 3rd order
Cbayesopt::MaternARD5	Matern ARD kernel of 5th order
Cbayesopt::SEArd	Square exponential (Gaussian) kernel
Cbayesopt::ConstKernel	Constant kernel
Cbayesopt::HammingKernel	Kernel for categorical data
►Cbayesopt::ISOkernel	Abstract class for isotropic kernel functors
Cbayesopt::MaternIso1	Matern isotropic kernel of 1st order
Cbayesopt::MaternIso3	Matern kernel of 3rd order
Cbayesopt::MaternIso5	Matern isotropic kernel of 5th order
Cbayesopt::RQIso	Rational quadratic (Student's t) kernel
Cbayesopt::SEIso	Square exponential (Gaussian) kernel
Cbayesopt::LinKernel	Linear kernel
Cbayesopt::LinKernelARD	Linear kernel
►Cbayesopt::Polynomial	Polynomial covariance function
Cbayesopt::Polynomial2
Cbayesopt::Polynomial3
Cbayesopt::Polynomial4
Cbayesopt::Polynomial5
Cbayesopt::Polynomial6
Cbayesopt::Polynomial7
►Cbayesopt::CombinedKernel	Abstract class for combined kernel
Cbayesopt::KernelProd	Product of two kernels
Cbayesopt::KernelSum	Sum of two kernels
Ckernel_parameters	Kernel configuration parameters
Cbayesopt::KernelFactory	Factory model for kernel functions This factory is based on the libgp library by Manuel Blum https://bitbucket.org/mblum/libgp which follows the squeme of GPML by Rasmussen and Nickisch http://www.gaussianprocess.org/gpml/code/matlab/doc/
Cbayesopt::KernelModel
Cbayesopt::KernelParameters
CLog< T >
▼CLog< Output2FILE >
CFILELog
▼CMatPlot
Cbayesopt::utils::DisplayProblem1D
Cbayesopt::utils::DisplayProblem2D
Cbayesopt::MCMCSampler	Markov Chain Monte Carlo sampler
Cmean_parameters
Cbayesopt::MeanFactory	Factory model for parametric functions This factory is based on the libgp library by Manuel Blum https://bitbucket.org/mblum/libgp which follows the squeme of GPML by Rasmussen and Nickisch http://www.gaussianprocess.org/gpml/code/matlab/doc/
Cbayesopt::MeanModel
Cbayesopt::MeanParameters
Cbayesopt::NLOPT_Optimization
▼Cobject
►Cbayesoptmodule.BayesOptContinuous	Python Module for BayesOptContinuous
Cdemo_distance.BayesOptTest
Cdemo_multiprocess.BayesOptProcess
Cdemo_quad.BayesOptTest
COutput2FILE
Cbayesopt::Parameters
▼Cbayesopt::ParametricFunction	Interface for mean functors
►Cbayesopt::AtomicFunction	Abstract class for an atomic kernel
Cbayesopt::ConstantFunction	Constant function
Cbayesopt::LinearFunction	Linear combination function
Cbayesopt::LinearPlusConstantFunction	Linear combination plus constant function
Cbayesopt::OneFunction	Constant one function
Cbayesopt::ZeroFunction	Constant zero function
►Cbayesopt::CombinedFunction	Abstract class for combined functions
Cbayesopt::SumFunction	Sum of two kernels
Cbayesopt::utils::ParamLoader
▼Cbayesopt::PosteriorModel	Bayesian optimization using different non-parametric processes as distributions over surrogate functions
Cbayesopt::EmpiricalBayes	Bayesian optimization using different non-parametric processes as distributions over surrogate functions
Cbayesopt::MCMCModel	Posterior model of nonparametric processes/criteria based on MCMC samples
Cbayesopt::PosteriorFixed	Bayesian optimization using different non-parametric processes as distributions over surrogate functions
▼Cbayesopt::ProbabilityDistribution
Cbayesopt::GaussianDistribution
Cbayesopt::StudentTDistribution
▼Cbayesopt::RBOptimizable
Cbayesopt::CritCallback
►Cbayesopt::NonParametricProcess	Abstract class to implement Bayesian regressors
►Cbayesopt::KernelRegressor	Abstract class to implement non-parametric processes
►Cbayesopt::ConditionalBayesProcess	Empirical Bayesian NonParametric process
Cbayesopt::GaussianProcess	Standard zero mean gaussian process with noisy observations
►Cbayesopt::HierarchicalGaussianProcess	Virtual class for hierarchical Gaussian processes
Cbayesopt::GaussianProcessML	Gaussian process with ML parameters
Cbayesopt::GaussianProcessNormal	Gaussian process with normal prior on the parameters
Cbayesopt::StudentTProcessJeffreys	Student T process with Jeffreys prior
Cbayesopt::StudentTProcessNIG	Student's t process with Normal Inverse-Gamma hyperprior on mean and snigal variance parameters
Cbayesopt::RBOptimizableWrapper
Cbayesopt::RGBOptimizable
Cbayesopt::RGBOptimizableWrapper
CTemplateWritter
Cuser_function_data
▼CProcess
Cdemo_multiprocess.BayesOptProcess