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Using multi-agent systems to represent uncertainty.
How do we represent uncertainty and prior knowledge?
Using multi-agent systems to represent uncertainty (summary of invited talk).
Finding ways to accurately and effectively represent uncertainty is one of the most important challenges in data visualization today.
The model uses Monte Carlo simulations to represent uncertainty in future hydrogen and electricity prices.
The thing was the needle wasn't swinging to represent statistical likelihood; it was a hard-coded effect meant to represent uncertainty in the statistical forecast.
These use random numbers (that's all "stochastic" means) to represent uncertainty introduced into our forecast by the unresolved small-scale processes.
Therefore, the deterministic analysis fails to properly represent uncertainty and variability, so common in engineering geology studies.
Yet in others, like direction learning and topic modeling, the matrix parameter instead pops up in the algorithms as the natural tool to represent uncertainty.
In the work described here, we use logarithmic transformations of the attenuation factor (AF), a simple process-based index model, to represent uncertainty in a pesticide leaching assessment.
In this approach, a set of local controllers is designed for a family of multi-models to represent uncertainty bounds over a flight envelope.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com