Download the latest prebuilt READY-TO-USE ParaMonte MATLAB library from the GitHub release page for Windows OS:
or for macOS / Linux:
For an illustration of the many powerful features of the library as well as serial and parallel example simulations see:
For more examples, see:
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 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 Python, MATLAB, 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 above. However, it should be compatible with MATLAB >=R2016b. If you find incompatibilities with any of the MATLAB releases newer than R2016a, please let us know by opening an issue on the GitHub issues page:
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.
See this page: https://www.cdslab.org/paramonte/notes/overview/preface/#how-to-acknowledge-the-use-of-the-paramonte-library-in-your-work
fantastic! This package provides the ultimate Monte Carlo simulation environment and MCMC sampler that I have been looking for, from the automated simulation setup to the postprocessing and visualization of the results. It has the simulation restart ability which is great in case the simulation gets interrupted at any point before the completion. What has been the most useful in my research is the possibility of running the code in parallel to speed up the simulation and the fact that the package took care of all the parallelization without needing me to do anything in parallel.
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