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

Chris Cannella, Mohammadreza Soltani, Vahid Tarokh

Keywords: Conditional Sampling, Normalizing Flows, Markov Chain Monte Carlo, Missing Data Inference

Abstract: We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. 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.

One-sentence Summary: We introduce and demonstrate a novel MCMC technique for sampling from the exact conditional distributions known by normalizing flows.

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Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CelebA](https://paperswithcode.com/dataset/celeba), [MNIST](https://paperswithcode.com/dataset/mnist)