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arXiv · 2010.08509

A Latent Slice Sampling Algorithm

Yanxin Li·Stephen G. Walker
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Abstract

In this paper we introduce a new sampling algorithm which has the potential to be adopted as a universal replacement to the Metropolis--Hastings algorithm. It is related to the slice sampler, and motivated by an algorithm which is applicable to discrete probability distributions %which can be viewed as an alternative to the Metropolis--Hastings algorithm in this setting, which obviates the need for a proposal distribution, in that is has no accept/reject component. This paper looks at the continuous counterpart. A latent variable combined with a slice sampler and a shrinkage procedure applied to uniform density functions creates a highly efficient sampler which can generate random variables from very high dimensional distributions as a single block.

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Yanxin Li, Stephen G. Walker. 2020-10-16. A Latent Slice Sampling Algorithm. https://arxiv.org/abs/2010.08509

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