Table of Contents
- CorrVectorData — Calculates correlation between data point x and other data points, that were used for learning of kriging model.
- EGO — Creates EGO optimizer.
- EGO_ask — Ask optimizer for next data point.
- EGO_best — Returns the best known point and corresponding criterion value.
- EGO_stop — Ask optimizer if stopping criterion has been met.
- EGO_tell — Tell EGO optimizer new data point and criterion value.
- EI — Calculates (one point) Expected improvement.
- EstimateVar — Calculates process variance and noise for kriging model.
- LL_driver_optim — Returns negative log Likelihood for kriging model as a function of kriging parameters for linking with new optimization packages.
- LOOCV — Calculates Leave One Out Cross-validation for given kriging model.
- RLHS — Creates RLH sampling of maxiter points for n dimensions in the interval [lob,upb].
- SimCondGaussian — Creates conditional gaussian process simulations.
- createCorr — Creates correlation matrix R.
- decompose_estBeta — Preforms Cholesky decomposition for covariance/correlation matrix. Calculates T_kriging M_kriging Ty_kriging frf_kriging Tyfb_kriging and estimates Beta_kriging if necessary.
- findTheta — Optimizes over kriging hyper-parameters (corelation scale parameters (theta) and/or process variance (sigma) or proporionality coefficent between process variance and noise (alfa)), by minimizing negative Likelihood or minimizing Leave One Out Cross validation.
- km — Creates kriging model, based on given parameters. It may estimate some parameters analytically.
- logLL — Calculates negative log Likelihood.
- poly0 — Constant trend function.
- poly1 — Linear trend function.
- poly2 — Quadratic trend function.
- predict — Calculates predicted kriging mean and variance at points x;
- rescale — Rescales X.
- showModel — Displays information about kriging model.
- simGaussian — Creates Gaussian process simulations based on given covariance and mean information.