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Parallel MCMC Without Embarrassing Failures
[article]
2022
arXiv
pre-print
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub)posteriors defined on data partitions. Then, a server combines local results. While efficient, this framework is very sensitive to the quality of subposterior sampling. Common sampling problems such as missing modes or misrepresentation of low-density regions are amplified -- instead of being
arXiv:2202.11154v2
fatcat:ssc5p6ox7fb4piq4talslnfuxa