State-Coupled Volatility in Latent Dynamical Systems: Recovery Under Partial Observation
Latent state-space models are widely used to study partially observed dynamical systems, yet most formulations assume that process variability is independent of latent-state position. In many biological, behavioral, and physiological systems, however, variability may depend systematically on the underlying dynamical state, producing structured stochasticity that is not captured by constant-variance models. We introduce a state-coupled stochastic volatility framework in which latent process variance depends on displacement from a latent equilibrium. To estimate this relationship under partial observation, we develop a particle expectation-maximization procedure combining bootstrap particle...
| Authors | Imani Beckett |
|---|---|
| Primary category | stat.ML |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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