arXiv:2605.21805v1 Announce Type: cross Abstract: 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, suff
This is a pre-print detailing a novel methodological approach in computational statistics for a specific class of models.
For a sophisticated reader, this represents incremental academic progress in statistical machine learning, rather than a significant event for strategic intelligence.
No immediate or foreseeable change to broader technological or economic landscapes is indicated by this academic paper alone.
Ongoing academic interest in neural estimation methods for complex probabilistic models continues.
Potentially, improved computational methods for state-space models could aid various scientific fields over a long time horizon.
Further research might eventually integrate such methods into practical applications requiring estimation in dynamic systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG