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Robustification is a form of optimisation whereby a system is made less sensitive to the effects of random variability, or noise, that is present in that system's input variables and parameters. The process is typically associated with engineering systems , but the process can also be applied to a political policy , a business strategy or any ...
Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution.
A robust parameter design, introduced by Genichi Taguchi, is an experimental design used to exploit the interaction between control and uncontrollable noise variables by robustification—finding the settings of the control factors that minimize response variation from uncontrollable factors. [1]
Robustness is the property of being strong and healthy in constitution. When it is transposed into a system, it refers to the ability of tolerating perturbations that might affect the system's functional body.
For example, Taguchi's ... This is sometimes called robustification. ... This page was last edited on 28 October 2024, at 16:29 (UTC).
Sample article layout (click on image for larger view) This guide presents the typical layout of Wikipedia articles, including the sections an article usually has, ordering of sections, and formatting styles for various elements of an article. For advice on the use of wiki markup, see Help:Editing; for guidance on writing style, see Manual of ...
Wikipedia's article classification system sorts articles by overall quality, and thus is a less obtrusive system for measuring quality, and is used instead of most tags. For example, most C-class articles lack enough references to be B-class, so tagging a C-class article with an article-wide tag for lack of references is nearly always redundant.
Bayes linear statistics is a subjectivist statistical methodology and framework. Traditional subjective Bayesian analysis is based upon fully specified probability distributions, which are very difficult to specify at the necessary level of detail.