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arXiv · 2007.06140

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

Chris Cannella·Mohammadreza Soltani·Vahid Tarokh
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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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Chris Cannella, Mohammadreza Soltani, Vahid Tarokh. 2020-07-13. Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows. https://arxiv.org/abs/2007.06140

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