A note on the Metropolis-Hastings acceptance probabilities for mixture spaces
Tobias Siems, Lisa Koeppel

TL;DR
This paper clarifies the implementation of Metropolis-Hastings acceptance probabilities in mixture spaces, introduces a generalized reversible jump algorithm, and compares different strategies for designing efficient trans-dimensional samplers.
Contribution
It provides a generalized framework for Metropolis-Hastings in mixture spaces, introduces new translation techniques, and revisits maximal acceptance probabilities for better algorithm classification.
Findings
Generalization of reversible jump algorithm
Introduction of a new translation technique
Comparison of sampler performance on change point example
Abstract
This work is driven by the ubiquitous dissent over the abilities and contributions of the Metropolis-Hastings and reversible jump algorithm within the context of trans dimensional sampling. We demystify this topic by taking a deeper look into the implementation of Metropolis-Hastings acceptance probabilities with regard to general mixture spaces. Whilst unspectacular from a theoretical point of view, mixture spaces gave rise to challenging demands concerning their effective exploration. An often applied but not extensively studied tool for transitioning between distinct spaces are so-called translation functions. We give an enlightening treatment of this topic that yields a generalization of the reversible jump algorithm and unveils another promising translation technique. Furthermore, by reconsidering the well-known Metropolis within Gibbs paradigm, we come across a dual strategy to…
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Taxonomy
TopicsAdvanced Topology and Set Theory · Advanced Banach Space Theory · Advanced Harmonic Analysis Research
