LEARNING MARKOV PROCESSES WITH LATENT VARIABLES
研究了仅观测二元马尔可夫链中一个变量时，如何从四个或更多连续观测的联合分布中唯一识别转移核和初始分布（潜变量状态标签可重排）。适用于短面板和（平稳）时间序列数据。
We consider the problem of identifying the parameters of a time-homogeneous bivariate Markov chain when only one of the two variables is observable. We show that, subject to conditions that we spell out, the transition kernel and the distribution of the initial condition are uniquely recoverable (up to an arbitrary relabelling of the state space of the latent variable) from the joint distribution of four (or more) consecutive time-series observations. The result is, therefore, applicable to (short) panel data as well as to (stationary) time series data.