MissBGM: 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 tabular data imputation method via Bayesian generative modeling that combines the 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 jointly models the data-generating and missingness mechanisms explicitly, producing posterior distribution over each missing entry. We develop a novel stochastic optimization algorithm with alternating updates of missing values, model parameters, and latent variables until convergence. For the theoretical analysis, we establish convergence to a uniquely separated pseudo-true tempered target under explicit criterion, approximation, and optimization conditions. Empirically, MissBGM outperforms classical and neural imputers across a range of settings. These results establish MissBGM as a principled and scalable solution for modern missing data imputation.