| computeCholeskyCorrelation() | bayesopt::KernelRegressor | protected | 
  | computeCorrMatrix(matrixd &corrMatrix) | bayesopt::KernelRegressor | inlineprotected | 
  | computeCorrMatrix() | bayesopt::KernelRegressor | inlineprotected | 
  | computeCrossCorrelation(const vectord &query) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlineprotected | 
  | computeDerivativeCorrMatrix(int dth_index) | bayesopt::KernelRegressor | protected | 
  | computeSelfCorrelation(const vectord &query) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlineprotected | 
  | ConditionalBayesProcess(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::ConditionalBayesProcess) | bayesopt::ConditionalBayesProcess |  | 
  | create(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) | bayesopt::NonParametricProcess | static | 
  | d_ | bayesopt::GaussianProcessNormal | private | 
  | dim_ (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected | 
  | evaluate(const vectord &x) | bayesopt::ConditionalBayesProcess | inlinevirtual | 
  | evaluateKernelParams() | bayesopt::ConditionalBayesProcess |  | 
  | fitSurrogateModel() | bayesopt::KernelRegressor | inlinevirtual | 
  | GaussianProcessNormal(size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal |  | 
  | getData() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | inline | 
  | getHyperParameters() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlinevirtual | 
  | getSignalVariance() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor |  | 
  | getValueAtMinimum() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | inline | 
  | HierarchicalGaussianProcess(size_t dim, Parameters params, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::HierarchicalGaussianProcess) | bayesopt::HierarchicalGaussianProcess |  | 
  | KernelRegressor(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor |  | 
  | mD (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private | 
  | mData (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected | 
  | mInvVarW (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private | 
  | mKernel (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected | 
  | mKF (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private | 
  | mL | bayesopt::KernelRegressor | protected | 
  | mLearnAll (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected | 
  | mLearnType (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected | 
  | mMean (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | protected | 
  | mScoreType (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | protected | 
  | mSigma | bayesopt::NonParametricProcess | protected | 
  | mVf | bayesopt::GaussianProcessNormal | private | 
  | mW0 (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | private | 
  | mWMap | bayesopt::GaussianProcessNormal | private | 
  | negativeLogLikelihood() | bayesopt::GaussianProcessNormal | privatevirtual | 
  | negativeTotalLogLikelihood() | bayesopt::HierarchicalGaussianProcess | protectedvirtual | 
  | nHyperParameters() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlinevirtual | 
  | NonParametricProcess(size_t dim, Parameters parameters, const Dataset &data, MeanModel &mean, randEngine &eng) (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess |  | 
  | precomputePrediction() | bayesopt::GaussianProcessNormal | privatevirtual | 
  | prediction(const vectord &query) | bayesopt::GaussianProcessNormal | virtual | 
  | RBOptimizable() (defined in bayesopt::RBOptimizable) | bayesopt::RBOptimizable | inline | 
  | setHyperParameters(const vectord &theta) (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | inlinevirtual | 
  | updateSurrogateModel() | bayesopt::KernelRegressor | virtual | 
  | ~ConditionalBayesProcess() (defined in bayesopt::ConditionalBayesProcess) | bayesopt::ConditionalBayesProcess | virtual | 
  | ~GaussianProcessNormal() (defined in bayesopt::GaussianProcessNormal) | bayesopt::GaussianProcessNormal | virtual | 
  | ~HierarchicalGaussianProcess() (defined in bayesopt::HierarchicalGaussianProcess) | bayesopt::HierarchicalGaussianProcess | inlinevirtual | 
  | ~KernelRegressor() (defined in bayesopt::KernelRegressor) | bayesopt::KernelRegressor | virtual | 
  | ~NonParametricProcess() (defined in bayesopt::NonParametricProcess) | bayesopt::NonParametricProcess | virtual | 
  | ~RBOptimizable() (defined in bayesopt::RBOptimizable) | bayesopt::RBOptimizable | inlinevirtual |