DeepAI AI Chat
Log In Sign Up

Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library

by   Amir Shahmoradi, et al.

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


page 4

page 6

page 7

page 10


ParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortran

ParaMonte (standing for Parallel Monte Carlo) is a serial and MPI/Coarra...

emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC

emcee is a Python library implementing a class of affine-invariant ensem...

PyTracer: Automatically profiling numerical instabilities in Python

Numerical stability is a crucial requirement of reliable scientific comp...

MatDRAM: A pure-MATLAB Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo Sampler

Markov Chain Monte Carlo (MCMC) algorithms are widely used for stochasti...

Automatically Differentiable Random Coefficient Logistic Demand Estimation

We show how the random coefficient logistic demand (BLP) model can be ph...

Fast Implementation of a Bayesian Unsupervised Algorithm

In a recent paper, we have proposed an unsupervised algorithm for audio ...