CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method
| Autor: | Christopher J. Earls, Amy L. Cochran, John Taylor Chavis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Bayesian probability Posterior probability Model selection 01 natural sciences Parallel MCMC 03 medical and health sciences symbols.namesake QA76.75-76.765 0103 physical sciences Convergence (routing) RJMCMC Computer software 010306 general physics 030304 developmental biology Reversible jump Markov chain Monte Carlo methods 0303 health sciences Markov chain Markov chain Monte Carlo Reversible-jump Markov chain Monte Carlo Computer Science Applications Jacobian matrix and determinant symbols Algorithm Software |
| Zdroj: | SoftwareX, Vol 14, Iss, Pp 100664-(2021) |
| ISSN: | 2352-7110 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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