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A-NICE-MC: Adversarial Training for MCMC
[article]
2018
arXiv
pre-print
Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes which can lead to slow convergence, or hand-crafting of problem-specific proposals by an expert. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. First, we propose an efficient likelihood-free adversarial training method to train a Markov chain and mimic a given data distribution. Then, we leverage
arXiv:1706.07561v3
fatcat:2yhdhmjzrrfbxdy62a2pxgbxby