论文标题
投影潜在的马尔可夫链蒙特卡洛：归一化流程的有条件抽样
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
论文作者
论文摘要
我们介绍了预测的潜在马尔可夫链蒙特卡洛（PL-MCMC），这是一种从正常化流量学到的高维条件分布中采样的技术。我们证明，从与归一流流量相关的确切条件分布中，PL-MCMC的大都市杂物实现。作为有条件的采样方法，PL-MCMC可以使蒙特卡洛期望最大化（MC-EM）对不完整数据的归一化流量进行训练。通过将归一化流量应用于各种数据集的数据任务的实验测试，我们证明了PL-MCMC对从归一化流量进行条件采样的功效。
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.
