A Latent Slice Sampling Algorithm
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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 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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