用智能切换策略融合快速推断与高精度采样，提升大规模数据推理效率。
Amortized Bayesian Workflow
- 根据数据复杂度自动选择神经网络快速推断或精确MCMC方法
- 在数万组数据上实现高效推理，后验质量接近金标准
- 通过计算复用实现两种方法协同，适合高通量数据分析场景
贝叶斯推断常面临计算速度与采样精度的权衡。我们提出一种自适应工作流，将快速的近似推断与可靠的金标准MCMC方法相结合，在处理大量观测数据集时兼顾速度与精度。该方法利用严谨的诊断机制，动态决策每组数据采用生成式神经网络的快速推断，或必要时切换至较慢但保证准确的MCMC。通过跨步骤复用计算，工作流实现了近似推断与MCMC的协同优化。我们在多个合成及真实世界问题中验证了该方法的有效性，涵盖数万组数据集，显著提升了效率同时保持高后验质量。
原文摘要 · Abstract (English)
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve a favorable combination of both speed and accuracy when performing inference on many observed datasets. Our approach uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling via generative neural networks to slower but guaranteed-accurate MCMC when needed. By reusing computations across steps, our workflow synergizes amortized and MCMC-based inference. We demonstrate the effectiveness of this integrated approach on several synthetic and real-world problems with tens of thousands of datasets, showing efficiency gains while maintaining high posterior quality.
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