Computer experiment: Difference between revisions

Revision as of 12:25, 21 February 2024

Revision as of 16:27, 8 June 2024

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===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 [httphttps://wwwpatents.google.com/patentspatent/WO2013055257A1?cl=/en&hl=ru 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].

==See also==