用流模型精准估计模拟器的后验分布，提升贝叶斯推断效率与精度。
Causal Posterior Estimation
- 将图模型的条件依赖结构直接嵌入神经网络，增强后验逼近能力。
- 连续型架构实现常数时间采样，计算复杂度降至O(1)。
- 在多个模拟器模型上超越或媲美当前最优方法，适合高维参数推断。
我们提出因果后验估计（Causal Posterior Estimation, CPE），一种针对模拟器模型的贝叶斯推断新方法。这类模型的似然函数难以计算或代价过高，但可在给定参数下生成模拟输出。CPE采用基于归一化流（NF）的后验分布近似，将模型图结构所诱导的条件依赖关系显式引入神经网络中，从而提升近似精度。本文设计了离散与连续两种NF架构，并为连续情形提出一种常数时间采样方法，使采样复杂度降至O(1)，与离散型同阶。通过大量实验验证，相比仅从数据中学习依赖结构的方法，直接编码图结构能显著提升后验推断准确率，在多个基准模型上优于或匹配现有最优水平。
原文摘要 · Abstract (English)
We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, i.e., models where the evaluation of the likelihood function is intractable or too computationally expensive, but where one can simulate model outputs given parameter values. CPE utilizes a normalizing flow-based (NF) approximation to the posterior distribution which carefully incorporates the conditional dependence structure induced by the graphical representation of the model into the neural network. Thereby it is possible to improve the accuracy of the approximation. We introduce both discrete and continuous NF architectures for CPE and propose a constant-time sampling procedure for the continuous case which reduces the computational complexity of drawing samples to O(1) as for discrete NFs. We show, through an extensive experimental evaluation, that by incorporating the conditional dependencies induced by the graphical model directly into the neural network, rather than learning them from data, CPE is able to conduct highly accurate posterior inference either outperforming or matching the state of the art in the field.
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