W. Scott Shambaugh

Abstract

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.

Keywords

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

DOI

10.25080/majora-212e5952-024

Bibtex entry

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Copyright © 2022 W. Scott Shambaugh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.