A Bayesian multilevel hidden Markov model with Poisson-lognormal emissions for intense longitudinal count data
密集纵向计数数据的贝叶斯多层泊松对数正态隐马尔可夫模型
Hidden Markov models (HMMs) are probabilistic methods in which observations are seen as realizations of a latent Markov process with discrete states that switch over time. Moving beyond standard statistical tests, HMMs offer a statistical environment to optimally exploit the information present in multivariate time series, uncovering the latent dynamics that rule them. Here, we extend the Poisson HMM to the multilevel framework, accommodating variability between individuals…