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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.
Robust methods aim to achieve robust performance and/or stability in the presence of bounded modelling errors. The early methods of Bode and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness, [1] prompting research to improve them. This was the start of the theory of ...
Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing involves invalid or unexpected inputs. Alternatively, fault injection can be used to test robustness ...
There are many mechanisms that provide genome robustness. For example, genetic redundancy reduces the effect of mutations in any one copy of a multi-copy gene. [21] Additionally the flux through a metabolic pathway is typically limited by only a few of the steps, meaning that changes in function of many of the enzymes have little effect on fitness.
This essentially means almost all nodes must be removed in order to destroy the giant component, and large scale-free networks are very robust with regard to random failures. One can make intuitive sense of this conclusion by thinking about the heterogeneity of scale-free networks and of the hubs in particular.
Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters.
Robust decision methods seem most appropriate under three conditions: when the uncertainty is deep as opposed to well characterized, when there is a rich set of decision options, and the decision challenge is sufficiently complex that decision-makers need simulation models to trace the potential consequences of their actions over many plausible ...
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.