让昂贵模型的贝叶斯推断又快又准，关键是把代理模型的不确定性算进去。
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
- 用代理模型加速训练数据生成，同时显式建模其不确定性
- 在计算资源紧张时仍能保持可靠后验估计，支持快速重复推断
- 适合需要频繁做贝叶斯推断的高成本仿真场景
贝叶斯推断通常依赖大量模型评估来估算后验分布。传统方法如马尔可夫链蒙特卡洛（MCMC）和摊销贝叶斯推断（ABI）在面对计算代价高的模型时会变得极为耗时。尽管ABI可在训练后实现快速推断，但生成足够训练数据仍需数千次模型模拟，对昂贵模型不现实。代理模型通过低成本近似模拟，可生成大规模训练数据集。然而，其引入的近似误差和不确定性会导致后验估计过于自信。为此，我们提出不确定性感知的代理模型驱动摊销贝叶斯推断（UA-SABI）框架：结合代理建模与ABI，显式量化并传播代理模型的不确定性。实验表明，该方法即使在严格时间限制下，也能为计算昂贵模型实现可靠、快速且可重复的贝叶斯推断。
原文摘要 · Abstract (English)
Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortized Bayesian Inference (ABI) can become computationally challenging. While ABI enables fast inference after training, generating sufficient training data still requires thousands of model simulations, which is infeasible for expensive models. Surrogate models offer a solution by providing approximate simulations at a lower computational cost, allowing the generation of large data sets for training. However, the introduced approximation errors and uncertainties can lead to overconfident posterior estimates. To address this, we propose Uncertainty-Aware Surrogate-based Amortized Bayesian Inference (UA-SABI) -- a framework that combines surrogate modeling and ABI while explicitly quantifying and propagating surrogate uncertainties through the inference pipeline. Our experiments show that this approach enables reliable, fast, and repeated Bayesian inference for computationally expensive models, even under tight time constraints.
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