AI RESEARCH
Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
arXiv CS.LG
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ArXi:2605.01676v1 Announce Type: cross 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.