MATDRAM: Delayed-Rejection Adaptive Metropolis MCMC

MatDRAM is a pure-MATLAB Adaptive Markov Chain Monte Carlo simulation and visualization library.

Download the latest prebuilt READY-TO-USE ParaMonte::MatDRAM library from the GitHub release page:

https://github.com/cdslaborg/paramonte/releases/latest/download/libparamonte_matdram.zip

For an illustration of the many powerful features of the library as well as serial and parallel example simulations see:

https://www.cdslab.org/paramonte/notes/examples/matlab/mlx/sampling_multivariate_normal_distribution_via_paradram.html

For more examples, see:

https://www.cdslab.org/paramonte/notes/examples/matlab/mlx/

Interested in receiving updates? Star and watch the GitHub repository of the library on GitHub:

https://github.com/cdslaborg/paramonte

If you find this package useful for your work, please rate it here and cite the ParaMonte library as described here:

https://www.cdslab.org/paramonte/notes/overview/preface/#how-to-acknowledge-the-use-of-the-paramonte-library-in-your-work

MatDRAM is a pure-MATLAB Monte Carlo simulation and visualization library for serial Markov Chain Monte Carlo simulations. MatDRAM contains a comprehensive implementation of the Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo (DRAM) sampler in the MATLAB environment.

For high-performance parallel simulations, visit the ParaMonte library's page on FileExchange:

https://www.mathworks.com/matlabcentral/fileexchange/78946-paramonte

or on GitHub:

https://github.com/cdslaborg/paramonte

MatDRAM is part of the ParaMonte library. 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 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:

https://www.cdslab.org/paramonte

MATLAB Release Compatibility:

This software has been only tested with MATLAB R2019a 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:

https://github.com/cdslaborg/paramonte/issues

This software is ready to use on all platforms: Windows/Linux/macOS.

If you wish to contribute to the development of the package, please fork the project on GitHub,

https://github.com/cdslaborg/paramonte

If you find any bugs or issues, please let us also know at:

https://github.com/cdslaborg/paramonte/issues