提出新方法提升状态空间模型参数推断的效率与稳定性。
Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models
- 用截断似然估计改进神经似然法，解决样本需求高、序列长度敏感问题。
- 在长序列上表现更优，训练更稳定，仅需少量模拟样本即达良好效果。
- 适合需要高效、可扩展推断的时序建模场景，如金融或生物系统分析。
状态空间模型（SSMs）是建模随时间变化系统的强大概率工具，其推断涉及对潜在状态和参数的估计。本文聚焦于参数推断，由于似然函数通常不可解析，该任务极具挑战性。近年来，基于神经网络的似然估计方法（如顺序神经似然，SNL）在贝叶斯推断中表现良好。然而，本文发现当应用于SSM时，SNL存在严重局限：需大量模拟样本才能达到中等性能，随序列长度增长而性能下降，且无法实现摊销推断。为此，我们提出一种新型算法——截断神经似然（T-SNL），解决了上述问题。T-SNL在准确性、训练稳定性、可扩展性方面均有显著提升，支持新观测下的摊销推断。实验表明，T-SNL是一种样本高效、鲁棒性强、灵活度高的算法，在多种基准测试中优于现有方法。
原文摘要 · Abstract (English)
State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the estimation of latent states and parameters. In this work, we focus on parameter inference, which for SSMs is in general a very challenging problem due to the intractability of the likelihood. Recently, neural estimation methods, such as sequential neural likelihood (SNL), have shown promising results in Bayesian inference problems. In this paper, we show that SNL, when applied to the SSM setting, suffers important limitations, such as requiring a large amount of simulated samples to achieve a moderate performance, scaling poorly with sequence length, while not being amortized. We then introduce a novel inference algorithm called truncated-SNL (T-SNL), which addresses the limitations of SNL. Our algorithm is more accurate, more stable and robust during training, more scalable to longer temporal sequences, and can be amortized when new observations become available. Our experiments show that T-SNL is sample-efficient, robust, and flexible algorithm which outperforms other approaches.
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