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Reversibility of elliptical slice sampling revisited

Statistics Theory 2024-05-07 v2 Machine Learning Computation Machine Learning Statistics Theory

Abstract

We extend elliptical slice sampling, a Markov chain transition kernel suggested in Murray, Adams and MacKay 2010, to infinite-dimensional separable Hilbert spaces and discuss its well-definedness. We point to a regularity requirement, provide an alternative proof of the desirable reversibility property and show that it induces a positive semi-definite Markov operator. Crucial within the proof of the formerly mentioned results is the analysis of a shrinkage Markov chain that may be interesting on its own.

Keywords

Cite

@article{arxiv.2301.02426,
  title  = {Reversibility of elliptical slice sampling revisited},
  author = {Mareike Hasenpflug and Viacheslav Telezhnikov and Daniel Rudolf},
  journal= {arXiv preprint arXiv:2301.02426},
  year   = {2024}
}

Comments

25 pages

R2 v1 2026-06-28T08:04:47.651Z