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 consistency guarantee, we present a general posterior stacking framework to make use of all available approximations. Our stacking method is able to combine densities, simulation draws, confidence intervals, and moments, and address the overall precision, calibration, coverage, and bias of the posterior approximation at the same time. We illustrate our method on several benchmark simulations and a challenging cosmological inference task.

翻译：基于仿真的推断在摊销贝叶斯计算中已广泛流行。在推断算法、模型架构不同，或仅因初始化和随机梯度存在随机性的情况下，通常会产生多个后验近似。我们提出一个具有一致性保证的通用后验堆叠框架，以利用所有可用的近似结果。该堆叠方法能够整合密度分布、仿真样本、置信区间及矩信息，同时解决后验近似的整体精度、校准性、覆盖率和偏差问题。我们通过多个基准仿真实验及一项具有挑战性的宇宙学推断任务验证了该方法的有效性。