Hidden Markov models with an unknown number of states and a repulsive prior on the state parameters
状态数未知且状态参数采用排斥先验的隐马尔可夫模型
Hidden Markov models (HMMs) offer a robust and efficient framework for analyzing time series data, modelling both the underlying latent state progression over time and the observation process, conditional on the latent state. However, a critical challenge lies in determining the appropriate number of underlying states, often unknown in practice. In this paper, we employ a Bayesian framework, treating the number of states as a random variable and employing reversible jump…