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.
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