Embed and Project : Discrete Sampling with Universal Hashing

  • A. U.S.
  • Published 2013

Abstract

We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model. We propose a sampling algorithm, called PAWS, based on embedding the set into a higher-dimensional space which is then randomly projected using universal hash functions to a lower-dimensional subspace… (More)

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