Extending finite mixture models with skew-normal distributions and hidden Markov models for time series
结合偏态正态分布与隐马尔可夫模型扩展时间序列有限混合模型
We introduce an extension of finite mixture models by incorporating skew-normal distributions within a Hidden Markov Model framework. By assuming a constant transition probability matrix and allowing emission distributions to vary according to hidden states, the proposed model effectively captures dynamic dependencies between variables. Through the estimation of state-specific parameters, including location, scale, and skewness, the proposed model enables the detection of…