Year: 2020
Type: preprint
Abstract: Slice sampling is an efficient Markov Chain Monte Carlo algorithm to sample from an unnormalized density with acceptance ratio always $1$. However, when the variable to sample is unbounded, its "stepping-out" heuristic works only locally, making it difficult to uniformly explore possible candidates. This paper proposes a simple change-of-variable method to slice sample an unbounded variable equivalently from [0,1).
Source: arXiv (Cornell University)
Author: Mochihashi, Daichi
Institution: The Institute of Statistical Mathematics
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3089642488