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Slice sampling is a Markov chain method and as such serves the same purpose as Gibbs sampling and Metropolis. Unlike Metropolis, there is no need to manually tune the candidate function or candidate standard deviation.
Recall that Metropolis is sensitive to step size. If the step size is too small [[random walk]] causes slow decorrelation. If the step size is too large there is great inefficiency due to a high rejection rate.
In contrast to Metropolis, slice sampling automatically adjusts the step size to match the local shape of the density function. Implementation is arguably easier and more efficient than Gibbs sampling or simple Metropolis updates.
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