Journal
SOFTWAREX
Volume 14, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.softx.2021.100664
Keywords
Reversible jump Markov chain Monte Carlo methods; RJMCMC; Model selection; Parallel MCMC
Categories
Funding
- Science Mathematics and Research for Transformation (SMART) program - USD/R&E, United States (Under Secretary of DefenseResearch and Engineering)
- National Defense Education Program (NDEP)/BA1, Basic Research, United States
- Alfred P. Sloan Foundation's Minority Ph.D. (MPHD) Program, United States [70481]
Ask authors/readers for more resources
CU-MSDSp is a parallel RJMCMC implementation that aims to increase accessibility of RJMCMC to practitioners. It independently forms Markov Chains to approximate the posterior distribution of model parameters, and uses these approximations to estimate the posterior distribution of the model space. This software eliminates the need for designing a trans-dimensional proposal distribution, while ensuring the same theoretical guarantees as the non-parallel algorithm.
Reversible jump Markov chain Monte Carlo (RJMCMC) is a powerful Bayesian trans-dimensional algorithm for performing model selection while inferring the distribution of model parameters. The present work introduces CU-MSDSp as an open source and fully automated parallel RJMCMC implementation that aims to increase the accessibility of RJMCMC to practitioners. CU-MSDSp begins by independently forming Markov Chains to approximate the posterior distribution of each model's parameters. These approximations are then used to estimate the posterior distribution of the model space. This embarrassingly parallelizable software eliminates the need of designing a trans dimensional proposal distribution and Jacobian all while ensuring the same theoretical guarantees as the non-parallel RJMCMC algorithm. Finally, CU-MSDSp enables practitioners to rely on their previous knowledge of fixed dimension MCMC convergence assessment and simulation design. (C) 2021 The Authors. Published by Elsevier B.V.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available