SwISS: A Scalable Markov chain Monte Carlo Divide‐and‐Conquer Strategy

@article{Vyner2022SwISSAS,
  title={SwISS: A Scalable Markov chain Monte Carlo Divide‐and‐Conquer Strategy},
  author={Callum Vyner and Christopher Nemeth and Chris Sherlock},
  journal={Stat},
  year={2022}
}
Divide-and-conquer strategies for Monte Carlo algorithms are an increasingly popular approach to making Bayesian inference scalable to large data sets. In its simplest form, the data are partitioned across multiple computing cores and a separate Markov chain Monte Carlo algorithm on each core targets the associated partial posterior distribution, which we refer to as a sub-posterior , that is the posterior given only the data from the segment of the partition associated with that core. Divide… 

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