Estimates 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.
[kmodel, perf]=findTheta(model,lob,upb,crit,method,maxiter,params);
mlist of type kmodel created previously using function km.
row vector (1*model.n). Lower boundaries for optimization over theta.
row vector (1*model.n). Upper boundaries for optimization over theta.
criterion for optimization.
if =='MLL' optimization uses Likelihood;
if == 'CV' optimization uses Leave-One-Out Cross-validation;
optimization method;
if == 'CMA' optimization is based on CMA-ES.
if == 'NM' optimization is based on Nelder-Mead method with restarts.
if == 'RS' optimization is based on random search.
if == 'RLHS' optimization is based on random Latin Hypercubes.
maximum number of criterion evaluations.
additional parameters for optimizer; If not given maxiter for number of maximum objective evaluations and for other default parameters are used.
if method == 'CMA', params may specify CMA-ES input structure.
if method == 'NM', params may specify parameters for Nelder-Mead method with restarts.
mlist of type kmodel, that contains updated kriging model with optimal parameters.
mlist structure of type perf containing data of performance of optimization.
iter - total number of criterion evaluations.
imprIter - number of iterations, where criterion value was improved.
models - list of kmodel objects for each iteration.
imprModels - list of kmodel objects for iterations, where criterion value was improved.
crit - column vector (perf.iter*1) containing criterion values for each iteration.
imprCrit - column vector (perf.imprIter*1) containing criterion values for iterations, where criterion value was improved.
Optimizes over correlation function's hyper-parameters of previously created kriging model by km(.). Determines corelation scale parameters (theta) and/or process variance (sigma) or proporionality coefficent between process variance and noise (alfa), by minimizing negative Likelihood or minimizing LOO Cross validation.
This function sets and uses global variables R_kriging T_kriging Ty_kriging Tyfb_kriging M_kriging Y_kriging frf_kriging Beta_kriging.