Global sensitivity analysis that takes into account correlations and dependencies in the LCA model during uncertainty propagation with Monte Carlo approach.
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Updated
May 27, 2024 - Python
Error (or uncertainty) propagation is the practice of analyzing and accounting for the effect of numeric quantities' uncertainties on the results of functions that involve them.
When variables used in a function or mathematical operation have errors (due to measurement uncertainties, random fluctuations, sample variance, etc.), error propagation can be used to determine the resulting error of the function's output.
Global sensitivity analysis that takes into account correlations and dependencies in the LCA model during uncertainty propagation with Monte Carlo approach.
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