AI RESEARCH
Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
arXiv CS.LG
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ArXi:2606.02231v1 Announce Type: cross 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 Marko Switching Model (MSM), a class of Hidden Marko Models with autoregressive dependencies among latent regimes and observed variables.