Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models
For researchers working on simulation-based inference in state-space models, this work provides a more efficient and robust algorithm that addresses key bottlenecks of existing neural methods.
The paper identifies limitations of sequential neural likelihood (SNL) for parameter inference in state-space models, including poor sample efficiency and scalability. It proposes truncated-SNL (T-SNL), which improves accuracy, stability, scalability, and amortization, outperforming existing methods.
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.