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.

翻译：状态空间模型（SSM）是建模包含潜在动态的时变系统的强大概率工具。SSM中的推理涉及潜在状态与参数的估计。本研究聚焦于参数推断——由于似然函数的不可解性，这对SSM而言通常极具挑战性。近期，序列神经似然（SNL）等神经估计方法在贝叶斯推理问题中展现出良好前景。本文证明，SNL在应用于SSM场景时存在显著局限：例如需要大量模拟样本才能获得中等性能、随序列长度扩展性差、且无法实现摊销。为此，我们提出名为截断式序列神经似然（T-SNL）的新型推理算法，该算法克服了SNL的上述缺陷。我们的算法在训练过程中具有更高的精度、稳定性和鲁棒性，对更长时序序列具有更好的可扩展性，且在新观测数据出现时可实现摊销。实验表明，T-SNL是一种样本高效、鲁棒且灵活的算法，其性能优于其他方法。