 Under such a setup, we eventually obtain a nonstationary HMM the transition probabilities of which evolve over time in a manner that is inferred from the data itself, as opposed to some unrealistic ad-hoc model of temporal evolution.The model suitable in the context of longitudinal data is named latent Markov model.The basic version of this model has been extended to include individual covariates, random effects and to model more complex data structures such as multilevel data.