Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
Temporal systems often exhibit non-stationary behaviour, such as seasonal climate variation or glucose fluctuations in patients with type-1 diabetes. One way to model non-stationarity is through discrete latent regimes, i.e., stationary segments of time. Such systems induce a Markov Switching Model (MSM), a class of Hidden Markov Models with autoregressive dependencies among latent regimes and observed variables. Identifying latent regimes is challenging in the presence of frequent regime switches and nonlinear and non-Gaussian dynamics, particularly when there are instantaneous effects between the variables, e.g., due to slow rates of measurements. In this work, we establish the identifi...
| Authors | Roel Hulsman, Carles Balsells-Rodas, Sara Magliacane |
|---|---|
| Primary category | stat.ML |
| Published | 1 June 2026 |
| Last updated at source | 12 June 2026 |
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