The article introduces a new model that can analyze data with hidden variables. By expanding the types of distributions considered and allowing for linear relationships between random effects and hidden variables, this model can handle various types of data, not just normal distributions. This approach reveals the flexibility in choosing the distribution of hidden variables and opens up possibilities for non-linear models. This model also sets the stage for predicting these hidden variables in future research.