Roll the dice! Quantify uncertainty and sensitivities in your existing computational models with the “monaco” Monte Carlo library. Users define input variables randomly drawn from any of SciPy's statistical distributions, run their model in parallel anywhere from 1 to millions of times, and postprocess the outputs to obtain meaningful, statistically significant conclusions. This talk will go over why you should always be running Monte Carlos, a demo of how to set up and run a sim, and a crash course in generating relevant plots and statistics.
Project repo: https://github.com/scottshambaugh/monaco
Lots of examples: https://github.com/scottshambaugh/monaco/tree/main/examples
API Documentation: https://monaco.readthedocs.io
Conference paper: https://conference.scipy.org/proceedings/scipy2022/pdfs/scott_shambaugh.pdf
Talk slides and notebooks: https://github.com/scottshambaugh/monaco-scipy2022