Unlike physical experiments, it is not uncommoncommon for computer experiments to have thousands of different input combinations. Because the standard inference requires [[Invertible matrix|matrix inversion]] of a square matrix of the size of the number of samples (<math>n</math>), the cost grows on the <math> \mathcal{O} (n^3) </math>. Matrix inversion of large, dense matrices can also cause induce numerical inaccuracies. Currently, this problem is solved by greedy decision tree techniques, allowing effective computations for unlimited dimensionality and sample size [http://www.google.com/patents/WO2013055257A1?cl=en&hl=ru patent WO2013055257A1], or avoided by using approximation methods, e.g. [http://www.stat.wisc.edu/~zhiguang/Multistep_AOS.pdf].
