This toolbox implements kriging based regression (also known as Gaussian process
regression) and optimization of deterministic simulators.
The toolbox consists of two main components:
1. functions for creation of kriging model for deterministic or noisy data
(correlation kernels, hyper-parameter estimation, prediction, cross
2. methods implementing Efficient Global Optimization (EGO) for time consuming
The main functions for creation and working with kriging model:
km - create kriging model.
findTheta - find (learn) kriging hyper-parameters.
predict - calculate prediction of kriging mean and variance.
LOOCV - calculate Leave One Out Cross-validation for given kriging model.
simGaussian - simulate Gaussian processes.
SimCondGaussian - simulate Gaussian processes conditional on observations.
Included trend functions: constant, linear, quadratic polynomials.
Included correlation kernels: Gaussian, Matern_5_2, power exponential (p=1.9)
and exponential (p=1);
The main functions for optimization:
EGO- create EGO optimizer.
EGO_ask - get next data points.
EGO_tell - to tell optimizer response values at new data points.
EGO_stop - indicates if stopping criterion has been reached.
EGO_best - returns the best point and criterion value.