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Monaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses

W. Scott Shambaugh


This paper introduces monaco, a Python library for conducting Monte Carlo simulations of computational models, and performing uncertainty analysis (UA) and sensitivity analysis (SA) on the results. UA and SA are critical to effective and responsible use of models in science, engineering, and public policy, however their use is uncommon. By providing a simple, general, and rigorous-by-default library that wraps around existing models, monaco makes UA and SA easy and accessible to practitioners with a basic knowledge of statistics.


Monte Carlo, Modeling, Uncertainty Quantification, Uncertainty Analysis, Sensitivity Analysis, Decision-Making, Ensemble Prediction, VARS, D-VARS



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