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Statistical analyses, in which uncertainty is introduced, are comparatively very scarce.
It contributes to a vicious circle, in which uncertainty discourages long-term planning by business and politicians alike.
Another way in which uncertainty about the future can be a source of knowledge is indirectly, through its adverse psychological consequences.
His research centers on computational algorithms, mathematical approximations, statistical methodology, and optimization methods for the analysis of systems in which uncertainty is present.
An attractive alternative solution is to use a fully stochastic model in which uncertainty is embedded from the outset.
Our results support a parallel processing model of cognitive control, in which uncertainty on multiple levels of a decision is reduced simultaneously.
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Previous prioritization methods suffer from cavities in which uncertainties were associated with morphometric variables of watershed ecosystem.
This study can be used for improving a manufacturing system in which uncertainties in tasks and inter-task times significantly degrade its productivity.
Non-probabilistic, interval analysis method in which uncertainties are defined by interval numbers appears as an alternative to the classical probabilistic models.
The problem is formulated using a stochastic robust optimization model in which uncertainties in the day-ahead market prices and in the driving requirements of electric vehicles are modeled using scenarios and confidence bounds, respectively.
A probabilistic analysis, in which uncertainties are explicitly modeled has been carried out to investigate the effects of modeling and input parameter uncertainties, and to evaluate their contribution to a system's vibratory response prediction.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com