On the Identifiability of Markov Switching Models

Abstract

In the realm of interpretability and out-of-distribution generalization, the identifiability of latent variable models has emerged as a captivating field of inquiry. In this work, we delve into the identifiability of Markov Switching Models, taking an initial stride toward extending recent results to sequential latent variable models. We develop identifiability conditions for first-order Markov dependency structures, whose transition distribution is parametrised via non-linear Gaussians. Through empirical studies, we demonstrate the practicality of our approach in facilitating regime-dependent causal discovery and segmenting high-dimensional time series data.