Identifiability of latent variable models has recently gained interest in terms of its applications to interpretability or out of distribution generalisation. In this work, we study identifiability of Markov Switching Models as a first step towards extending recent results to sequential latent variable models. We present identifiability conditions within first-order Markov dependency structures, and parametrise the transition distribution via non-linear Gaussians. Our experiments showcase the applicability of our approach for regime-dependent causal discovery and high-dimensional time series segmentation.

翻译：潜变量模型的可识别性因其在可解释性或分布外泛化中的应用而近期备受关注。本文作为将最新研究成果推广至序列潜变量模型的第一步,探讨了马尔可夫切换模型的可识别性问题。我们在一阶马尔可夫依赖结构内建立了可识别性条件,并通过非线性高斯分布对转移分布进行参数化。实验结果表明,该方法在状态依赖型因果发现与高维时间序列分割中具有应用价值。