• Corpus ID: 249017989

Stereographic Markov Chain Monte Carlo

@inproceedings{Yang2022StereographicMC,
  title={Stereographic Markov Chain Monte Carlo},
  author={Jun Yang and Krzysztof G. Latuszy'nski and Gareth O. Roberts},
  year={2022}
}
. High dimensional distributions, especially those with heavy tails, are notoriously difficult for off the shelf MCMC samplers: the combination of unbounded state spaces, diminishing gradient information, and local moves, results in empirically observed ”stickiness” and poor theoretical mixing properties – lack of geometric ergodicity. In this paper, we introduce a new class of MCMC samplers that map the original high dimensional problem in Euclidean space onto a sphere and remedy these notorious… 

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