论文标题
通过Paramonte :: Python库快速完全可复制的串行/平行蒙特卡洛和MCMC模拟和可视化
Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library
论文作者
论文摘要
Paramonte :: Python（在Python中代表平行的蒙特卡洛）是（马尔可夫链）蒙特卡洛（MCMC）的串行和MPI平行的库，用于采样数学目标功能，尤其是在数据科学，机器学习和机器学习和一般科学的科学构成中的参数和分析中的参数模型和分析的后验分布。除了提供快速高性能串行/并行蒙特卡洛和MCMC采样例程外，Paramonte :: Python库还提供了广泛的后处理和可视化工具，旨在自动化和简化贝耶斯数据分析中模型校准和不确定性定量的过程。此外，Paramonte :: Python采样器的自动重新启动功能可确保无缝完全确定性的蒙特卡洛模拟重新启动，如果发生任何中断。 Paramonte :: Python库是MIT-LICONENS的，并在https://github.com/cdslaborg/paramonte/tree/tree/master/master/src/src/interface/python上永久维护。
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.
