Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, different architectures, or simply the randomness of initialization and stochastic gradients. With a provable asymptotic guarantee, we present a general stacking framework to make use of all available posterior approximations. Our stacking method is able to combine densities, simulation draws, confidence intervals, and moments, and address the overall precision, calibration, coverage, and bias at the same time. We illustrate our method on several benchmark simulations and a challenging cosmological inference task.

翻译：基于模拟的推断在摊销贝叶斯计算中广受欢迎。在实践应用中，通常会产生多个后验近似结果，这些结果可能来自不同的推断算法、不同的架构，或仅仅源于初始化和随机梯度的随机性。我们提出了一种具有渐进性保证的通用堆叠框架，能够有效利用所有可用的后验近似。该堆叠方法可以整合密度函数、模拟抽样、置信区间和矩估计，同时提升整体精度、校准度、覆盖率和偏差控制。我们通过在多个基准模拟实验和一项具有挑战性的宇宙学推断任务中验证了该方法的有效性。