Perfect slice samplers

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How researchers see this paper

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A* Sampling (2014, methodology): “Some are specialized to specific distributions [5], but exact generic methods are based either on (adaptive) rejection sampling [6, 7, 8] or Markov Chain Monte Carlo (MCMC) methods where convergence to the stationary distribution can be guaranteed [9, 10, 11].”
Bayesian reasoning and machine learning (2012, unknown): “See also [198] for the use of auxiliary variables in perfect sampling.”
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods (2014, methodology): “Mira et al. (2013) showed, in particular, that any posterior density p(θ|y) approximating a Gaussian forms a suitable candidate for implementing the ZV scheme.”
MCMC Using Hamiltonian Dynamics (2011, background): “The coupling for slice sampling has been designed by [30] under the assumption that there exists a minimal element xmin ∈ S with respect with the order x ≺ y ⇔ f(x) ≤ f(y).”
Zero variance Markov chain Monte Carlo for Bayesian estimators (2010, background): “These modifications have been achieved in many different ways, for example by trying to induce negative correlation along the chain path ([6,21,13,41,12]); by trying to avoid random walk behavior via successive over-relaxation ([1,36,7]); by hybrid Monte Carlo ([16,35,10,18,27]); by exploiting non r”