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.
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Distributions vary according to the data being modeled. Human height follows a normal distribution, whereas concentrations of chemicals in the environment follow a lognormal distribution. If there are known boundaries, these distributions may also be expressed in a truncated form. Where information is lacking about the processes that generate the data, other possibilities exist. The uniform distribution (for example, the position of rain drops falling on a wire) gives equal probability to all values in a given range. The triangular distribution sets upper and lower limits and a preferred value somewhere between them.
Uncertainty analysis & Monte Carlo methods
Sean Salleh
Sean Salleh, a data scientist is experienced in guiding marketing strategy, forecasting models, scenario planning models, and algorithms. He has a master's degree in Operations Research from UC Irvine and Mathematics from Northeastern University.
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