  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.
