Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
Missing data imputation remains a fundamental challenge in modern data science, especially when uncertainty quantification is essential. In this work, we propose MissBGM, an AI-powered missing data imputation method via Bayesian generative modeling that bridges the expressive flexibility of neural networks with the statistical rigor of Bayesian inference. Unlike existing methods that often focus on point estimates or treat the missingness mechanism implicitly, MissBGM explicitly and jointly models the data-generating and missingness mechanisms, providing principled posterior uncertainty over imputations rather than a single point estimate. We develop a stochastic optimization framework wi...
| Authors | Qiao Liu |
|---|---|
| Primary category | stat.ML |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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