Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows

• URL: http://arxiv.org/abs/2007.06140v4
• Date: Fri, 26 Feb 2021 16:52:28 GMT
• Title: Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
• Authors: Chris Cannella, Mohammadreza Soltani, Vahid Tarokh
• Abstract summary: Projected Latent Markov Chain Monte Carlo (PL-MCMC) is a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data.
• Score: 37.87437571724747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow. We prove that a Metropolis-Hastings implementation of PL-MCMC asymptotically samples from the exact conditional distributions associated with a normalizing flow. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.

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