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Arbitrary multilevel models can now be amortized with neural networks that precisely preserve their full conditional independence structure, outperforming samplers on large-scale problems.
Continual self-consistency learning rescues amortized Bayesian inference from catastrophic forgetting, enabling robust posterior estimation even with sequentially arriving, real-world data.
