Version 1.5.1 (4.48 MB) by CDSLAB
ParaMonte: Plain Powerful Parallel Monte Carlo MCMC Library for Bayesian optimization in MATLAB, Python, Fortran, C++, C.
Updated 3 Jan 2021
DOWNLOAD BELOW, the latest prebuilt READY-TO-USE ParaMonte MATLAB library from the GitHub release page for:
curl -OL$libname.tar.gz
tar xvzf $libname.tar.gz && cd $libname
matlab # run matlab from the command line, then call the supplied "main" example script
For an illustration of the many powerful features of the library as well as serial and parallel example simulations see this example problem of sampling multivariate normal distribution via paradram.
For more examples, visit:
Interested in receiving updates? Star and watch the GitHub repository of the library on GitHub:
If you find this package useful for your work, please rate it here and cite the ParaMonte library publications as described here:
ParaMonte is a serial/parallel library of Monte Carlo simulation routines for stochastic optimization, sampling, and integration of mathematical objective functions of arbitrary-dimensions, in particular, the posterior probability distributions of Bayesian regression models in data science, Machine Learning, and scientific inference, with the design goal of unifying the automation (of Monte Carlo simulations), user-friendliness (of the library), accessibility (from multiple programming environments), high-performance (at runtime), and scalability (across many parallel processors).
The ParaMonte library currently includes ParaDRAM: a comprehensive implementation of the Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo (DRAM) sampler for both serial and parallel simulations in the MATLAB environment. In particular, the ParaMonte library enables you to run your simulations in parallel without writing a single-line of parallel code in the MATLAB environment, without even requiring the MATLAB parallelization toolbox.
The ParaMonte library has been designed to be blazing-fast while maintaining a high level of flexibility and user-friendliness.
The ParaMonte library is currently readily accessible from MATLAB, Python, Fortran, C++/C programming languages. For more information on the installation, usage, and examples, visit:
A pure-MATLAB implementation of the ParaDRAM algorithm of ParaMonte is also available as a separate library (named MatDRAM) here:
MATLAB Release Compatibility:
This software has been only tested with MATLAB R2018a and newer. It is not compatible with MATLAB <= R2017b. If you find incompatibilities with any of the MATLAB releases newer than R2018a, please let us know by opening an issue on the GitHub issues page:
Compatibility with MATLAB 2018a on Windows requires the MATLAB bug and security upgrade: MATLAB 2018a Update 6 for Windows. Some visualizations (such as histfit, and autocorrelation plots also require MATLAB's Statistics Toolbox as well as Econometrics Toolbox. However, these toolboxes are not essential for the ParaMonte Kernel library (the Monte Carlo and MCMC simulations).
This software is ready to use on x64-architecture computers (almost all of the recently-built computers are x64). If your platform is other than x64 or other than Windows/Linux/macOS, follow the simple guidelines here to build the library for your local machine. Please let us also know at:
so that we can support your platform and architecture in the future.

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MATLAB Release Compatibility
Created with R2020a
Compatible with R2018a and later releases
Platform Compatibility
Windows macOS Linux
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.