A Sampling Method Based on LDPC Codes

نویسندگان

  • Xuhong Zhang
  • Gregory W. Wornell
چکیده

Sampling is a powerful and popular way to tackle problems for which exact inference is computationally intractable. We propose a novel sampling scheme based on Low Density Parity Check (LDPC) codes which works for any distribution over a finite alphabet. Compared to existing methods such as Markov Chain Monte Carlo (MCMC) Methods, the proposed scheme offers a different trade-off among various features desirable of a sampling method. In particular, in addition to good scaling behaviour with dimensions, the proposed scheme has good control over independence properties and makes efficient utilization of randomness.

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تاریخ انتشار 2016