提出新方法在模拟不准确时仍能稳定推断后验分布。
Misspecification-robust amortised simulation-based inference using variational methods
- 用变分推断和误差建模弥合模拟与现实的差距。
- 在天文学真实数据上验证，即使模型有偏差也能可靠推断。
- 无需设定错误影响的超参数或先验，适合实际应用。
神经密度估计的进展使得模拟基于推断（SBI）方法能够灵活逼近复杂随机模型的贝叶斯推断。然而，当模拟器准确反映数据生成过程（DGP）时，这些方法表现良好；一旦存在模型误设，其性能显著下降。由于实际中模拟器总是以某种程度偏离真实DGP，这成为其应用的主要障碍。本文提出鲁棒变分神经后验估计（RVNP），通过变分推断与误差建模，解决摊销式SBI中的误设问题。我们在多个基准任务上测试了该方法，包括使用天文学的真实数据，结果表明，该方法能在不依赖描述误设影响的超参数或先验的前提下，以数据驱动方式恢复稳健的后验推断。
原文摘要 · Abstract (English)
Recent advances in neural density estimation have enabled powerful simulation-based inference (SBI) methods that can flexibly approximate Bayesian inference for intractable stochastic models. Although these methods have demonstrated reliable posterior estimation when the simulator accurately represents the underlying data generative process (DGP), recent work has shown that they perform poorly in the presence of model misspecification. This poses a significant issue for their use in real-world problems, due to simulators always misrepresenting the true DGP to a certain degree. In this paper, we introduce robust variational neural posterior estimation (RVNP), a method which addresses the problem of misspecification in amortised SBI by bridging the simulation-to-reality gap using variational inference and error modelling. We test RVNP on multiple benchmark tasks, including using real data from astronomy, and show that it can recover robust posterior inference in a data-driven manner without adopting hyperparameters or priors governing the misspecification influence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。