• Corpus ID: 88521229

Average of Recentered Parallel MCMC for Big Data

@article{Wu2017AverageOR,
  title={Average of Recentered Parallel MCMC for Big Data},
  author={Changye Wu and Christian P. Robert},
  journal={arXiv: Computation},
  year={2017}
}
In big data context, traditional MCMC methods, such as Metropolis-Hastings algorithms and hybrid Monte Carlo, scale poorly because of their need to evaluate the likelihood over the whole data set at each iteration. In order to resurrect MCMC methods, numerous approaches belonging to two categories: divide-and-conquer and subsampling, are proposed. In this article, we study the parallel MCMC and propose a new combination method in the divide-and-conquer framework. Compared with some parallel… 
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