CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method

• Main Authors: John Taylor Chavis, III, Amy Louise Cochran, Christopher James Earls
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: SoftwareX
• Subjects: Reversible jump Markov chain Monte Carlo methods; RJMCMC; Model selection; Parallel MCMC
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352711021000091
• ISSN: 2352-7110

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.