BayesNDE: Bayesian Generative Modeling for Neural Density Estimation

arXiv:2609.39843 · stat.ML, cs.AI, cs.LG, stat.ME · Submitted 2026-09-30 · Read on arXiv

stat.ML, cs.AI, cs.LG, stat.ME

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/liuq-lab/BayesNDE

Terminology

• Generative Model — 18× in this paper · explained in 2 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Generative Modeling — 13× in this paper · explained in 3 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Regularization — 8× in this paper · explained in 3 episodes
• Adam — 7× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 3 episodes
• Normalizing Flow — 6× in this paper · explained in 3 episodes
• Normalizing Flows — 6× in this paper · explained in 4 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Latent Representation — 4× in this paper · explained in 2 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Causal Inference — 2× in this paper · explained in 4 episodes
• Core — 2× in this paper · explained in 2 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Hamiltonian Monte Carlo — 1× in this paper
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
• An AI-powered Bayesian Generative Modeling Approach for Arbitrary Conditional Inference
• BGM-IV: AI-Powered Bayesian Generative Modeling for Instrumental Variable Regression with High-Dimensional Covariates

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