The slice sampler and centrally symmetric distributions

Christophe Planas and Alessandro Rossi

Monte Carlo Methods and Applications, 2024, vol. 30, issue 3, 299-313

Abstract: We show that the slice sampler generates Markov chains whose variables are mean independent and thus uncorrelated when the target density is centrally symmetric. Skewness instead boosts correlations. Popular implementation algorithms such as stepping-out and multivariate-sampling-with-hyperrectangles add statistical inefficiency, the first in case of multimodality, the second in all circumstances. A new sampler which exploits these structural and algorithmic characteristics to reduce the variance of Monte Carlo estimates is experimented in several sampling problems. An insight into the properties of the product slice sampler is also provided.

Keywords: Markov chain Monte Carlo; multivariate sampling; inefficiency factor (search for similar items in EconPapers)
Date: 2024
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Working Paper: The slice sampler and centrally symmetric distributions (2018)
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DOI: 10.1515/mcma-2024-2012

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