Editing Computer experiment (section)

===Problems with massive sample sizes===
Unlike physical experiments, it is common 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 numerical inaccuracies.  Currently, this problem is solved by greedy decision tree techniques, allowing effective computations for unlimited dimensionality and sample size [https://patents.google.com/patent/WO2013055257A1/en patent WO2013055257A1], or avoided by using approximation methods, e.g. [https://wayback.archive-it.org/all/20120130182750/http://www.stat.wisc.edu/~zhiguang/Multistep_AOS.pdf].