Normalizing flow-based variational inference (flow VI) is a promising approximate inference approach, but its performance remains inconsistent across studies. Numerous algorithmic choices influence flow VI's performance. We conduct a step-by-step analysis to disentangle the impact of some of the key factors: capacity, objectives, gradient estimators, number of gradient estimates (batchsize), and step-sizes. Each step examines one factor while neutralizing others using insights from the previous steps and/or using extensive parallel computation. To facilitate high-fidelity evaluation, we curate a benchmark of synthetic targets that represent common posterior pathologies and allow for exact sampling. We provide specific recommendations for different factors and propose a flow VI recipe that matches or surpasses leading turnkey Hamiltonian Monte Carlo (HMC) methods.

翻译：基于归一化流的变分推断是一种具有前景的近似推断方法，但其性能在不同研究中仍存在不一致性。众多算法选择会影响流变分推断的效能。我们通过逐步分析来解耦若干关键因素的影响：模型容量、目标函数、梯度估计器、梯度估计数量以及步长设置。每个步骤在利用前期分析见解和/或大规模并行计算的基础上，聚焦单一变量并控制其他因素。为支持高保真度评估，我们构建了一个合成目标基准测试集，该集合涵盖了典型的后验病理形态并支持精确采样。我们针对不同影响因素提出了具体建议，并设计了一套流变分推断方案，其性能达到或超越了当前主流的即用型哈密顿蒙特卡洛方法。