Uncertainty analysis is often a prominent part of studies for sectors such as the environment. The uncertainty itself is determined by a number of elements. They include available measurements of data to be used as input, identification of extreme or limit values of such data, knowledge of the distribution of the data and mechanisms affecting this, and any additional expert opinion that can be usefully added in. Uncertainty in the data itself may come from the definition of what data is to be collected or used, natural variability of the process generating the data, and uncertainty in measuring or sampling the data, or using reference data with incomplete descriptions.
Uncertainty analysis and Monte Carlo methods
Sean Salleh
Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.
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