Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library
This library addresses the need for efficient and reproducible Monte Carlo simulations in data science and scientific inference, though it appears incremental as an implementation of existing methods.
The authors developed ParaMonte::Python, a library for serial and parallel Monte Carlo/MCMC simulations that provides fast sampling routines, post-processing tools, and visualization capabilities for Bayesian modeling, with features like deterministic restart functionality.
ParaMonte::Python (standing for Parallel Monte Carlo in Python) is a serial and MPI-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for sampling mathematical objective functions, in particular, the posterior distributions of parameters in Bayesian modeling and analysis in data science, Machine Learning, and scientific inference in general. In addition to providing access to fast high-performance serial/parallel Monte Carlo and MCMC sampling routines, the ParaMonte::Python library provides extensive post-processing and visualization tools that aim to automate and streamline the process of model calibration and uncertainty quantification in Bayesian data analysis. Furthermore, the automatically-enabled restart functionality of ParaMonte::Python samplers ensure seamless fully-deterministic into-the-future restart of Monte Carlo simulations, should any interruptions happen. The ParaMonte::Python library is MIT-licensed and is permanently maintained on GitHub at https://github.com/cdslaborg/paramonte/tree/master/src/interface/Python.