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ParaMonte: A Powerful Serial/Parallel Monte Carlo and MCMC Library for Python, MATLAB, R, Fortran, C$++$, and C

ORAL

Abstract

Predictive science is a multilevel process requiring observational data and a model/hypothesis which will have to be calibrated and validated to eventually predict the quantity of interest. The primary method for model calibrations has, for decades, been that of Monte Carlo simulations. Here we present and discuss a collection of popular and powerful Monte Carlo techniques that can aid inference and uncertainty quantification in Machine learning and Bayesian problems that have both serial and parallel implementations within our package, ParaMonte. The primary focus in the development of ParaMonte has been on user-friendliness, accessibility from multiple programming languages and platforms, high-performance, parallelism and scalability, as well as reproducibility and comprehensive post-processing and visualization of the simulation results. Users can simply pass a user-made objective function to the samplers and upon completion, a series of files will be generated for comprehensive-reporting and post-processing of each simulation and its results. Automatic restart functionality is the core feature of all ParaMonte samplers and simulations. The ParaMonte library is permanently located at https://github.com/cdslaborg/paramonte.

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Authors

  • Joshua Osborne

    University of Texas at Arlington

  • Parvat Sapkota

    University of Texas at Arlington

  • Shashank Kumbhare

    University of Texas at Arlington

  • Chao Ma

    University of Texas at Arlington, Florida International University, Texas A&M University-Commerce, Texas A&M University–Commerce, University of Houston Downtown, Texas A\&M University, Carnegie Mellon University, Department of Physics, MSEC, Texas State University, Ingram School of Engineering, MSEC, Texas State University, MSEC, Texas State University, Ingram School of Engineering, Texas State University, Department of Chemistry and Biochemistry, North Carolina Central University, Durham, NC 27707, USA, Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA, Texas Tech University, Texas A&M University, University of San Francisco, University of Wuppertal, University of Illinois at Urbana-Champaign, University of Houston, University of Texas at Dallas, Mitchell Institute for Fundamental Physics and Astronomy, Texas A & M University, Center for Neutrino Physics, Department of Physics Virginia Tech, None, Department of Physics, University of Texas at Dallas, Department of Electrical Engineering, Yale University

  • Amir Shahmoradi

    University of Texas at Arlington