AggregateDataGP         Aggregate Data for Gaussian Process (GP)
                        Algorithms
AggregateDataTS         Aggregate Data for Thompson Sampling (TS)
                        Algorithm
AggregateDataUCB        GP Variants
BasisFunction           Basis Function
CovarianceFromKernel    Covariance Matrix from Kernel
GPTS                    Gaussian Process Thompson Sampling (GPTS)
                        Policy
GPTS_Mono               Gaussian Process Thompson Sampling Monotonic
                        (GPTS_Mono) Policy
GPUCB                   Gaussian Process Upper Confidence Bound (GPUCB)
                        Policy
GPUCB_Mono              Gaussian Process Upper Confidence Bound
                        Monotonic (GPUCB_Mono) Policy
GetDiagnostics          Get Experiment Diagnostics
JointCovFromKernel      Joint Covariance Matrix from Kernel
MABExperiment           Multi-Armed Bandit Experiment Framework
MakePosDefinitive       Positive Definite Covariance Matrix
NLML                    Gaussian Process Regression
NoiseSample             Noise Sampling for Heteroscedastic Gaussian
                        Processes
NonMonoPolicyEval       Evaluate Non-Monotonic Policies
OptimalHyperparameters
                        Optimal Hyperparameters
PolicyEvaluation        Evaluate Policies for Pricing Experiments
PosteriorPrediction     Posterior Prediction (Joint GP with
                        Derivatives)
PricingBandit           Run a Pricing Bandit Experiment
RBFKernel               Kernel Functions
RBFKernel_01            RBF Kernel (Point to Derivative)
RBFKernel_11            RBF Kernel (Derivative to Derivative)
RBFKernel_All           Generalized RBF Kernel
ResetDiagnostics        Reset Experiment Diagnostics
TS                      Thompson Sampling (TS) Policy
UCB                     Bandit Policies for Pricing Experiments
