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Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement
Journal of machine learning research
We develop ancestral Gumbel-Top-k sampling: a generic and efficient method for sampling without replacement from discrete-valued Bayesian networks, which includes multivariate discrete distributions, Markov chains and sequence models. The method uses an extension of the Gumbel-Max trick to sample without replacement by finding the top k of perturbed log-probabilities among all possible configurations of a Bayesian network. Despite the exponentially large domain, the algorithm has a complexitydblp:journals/jmlr/0001HW20 fatcat:yfaq5jel6jbatevfa6gbmrc34e