![]() Monte Carlo samplingĬode for performing Monte Carlo sampling (MCS) in matrix-based life cycle assessment (LCA) can be found here: In additon, a Taylor approximation of the original model can be used to propogate the uncertainties analytically. To propagate the input uncertainties, simulation techniques can be used such as Monte Carlo sampling, but more exotic sampling techniques, such as Latin hypercube sampling ( Figure 1) can also be used. Uncertainty propagation can be used to propagate uncertainties of input parameters through models to determine e.g. ![]() Uncertainty propagation (independent input parameters) Both the ipython notebook and the python scripts are written in Python 3. In case you don’t have access to MatLab, there is a free alternative called Octave available. ![]() the statistics toolbox, which is mentioned in the scripts). Note to the user: all MatLab code is written in MatLab R2014, and some require additional toolboxes (e.g.
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