用变分自编码器高效估算复杂后验分布，适合模拟推断场景。
Variational Autoencoders for Efficient Simulation-Based Inference
- 基于变分自编码器构建生成模型，用隐变量捕捉模拟数据的后验结构。
- 在基准测试中准确逼近复杂后验，计算效率优于传统方法。
- 提供两种先验策略：自适应先验提升泛化性，标准高斯先验更简洁。
我们提出一种基于变分推断框架的生成建模方法，用于无似然模拟推断。该方法利用变分自编码器中的隐变量，高效估计由随机模拟产生的复杂后验分布。我们探索了两种方法，区别在于先验处理方式：第一种通过多变量先验网络根据观测数据调整先验，增强对各类后验查询的泛化能力；第二种采用标准高斯先验，在保持简单性的同时仍能有效捕捉复杂后验结构。我们在经典基准问题上验证了该方法在逼近复杂后验时的准确性与计算效率。
原文摘要 · Abstract (English)
We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently estimate complex posterior distributions arising from stochastic simulations. We explore two variations of this approach distinguished by their treatment of the prior distribution. The first model adapts the prior based on observed data using a multivariate prior network, enhancing generalization across various posterior queries. In contrast, the second model utilizes a standard Gaussian prior, offering simplicity while still effectively capturing complex posterior distributions. We demonstrate the ability of the proposed approach to approximate complex posteriors while maintaining computational efficiency on well-established benchmark problems.
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