2022
DOI: 10.48550/arxiv.2205.12112
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Stereographic Markov Chain Monte Carlo

Abstract: 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 mixing problems.… Show more

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