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

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.

Automated summary

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.