Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood
MissCNF is introduced, which trains CNFs directly on incomplete data by maximizing the marginal likelihood of each partially observed sample, without discarding rows or constructing a completed dataset, and is compared with two common strategies for handling missing data.
Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann et al.
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