Reversible Genetically Modified Mode Jumping MCMC
Methodology
2021-10-18 v2 Computation
Machine Learning
Abstract
In this paper, we introduce a reversible version of a genetically modified mode jumping Markov chain Monte Carlo algorithm (GMJMCMC) for inference on posterior model probabilities in complex model spaces, where the number of explanatory variables is prohibitively large for classical Markov Chain Monte Carlo methods. Unlike the earlier proposed GMJMCMC algorithm, the introduced algorithm is a proper MCMC and its limiting distribution corresponds to the posterior marginal model probabilities in the explored model space under reasonable regularity conditions.
Keywords
Cite
@article{arxiv.2110.05316,
title = {Reversible Genetically Modified Mode Jumping MCMC},
author = {Aliaksandr Hubin and Florian Frommlet and Geir Storvik},
journal= {arXiv preprint arXiv:2110.05316},
year = {2021}
}
Comments
6 pages, 2 table, based on arXiv:1806.02160, which got divided into two revised articles