Description: A probabilistic latent variable model is a statistical approach that seeks to explain the relationships between observed variables through latent variables, which are those that cannot be directly measured. These latent variables represent underlying concepts that influence the observed variables, thus allowing for a deeper understanding of the data. This type of model is based on probability theory and uses mathematical techniques to infer the existence and impact of these hidden variables. Among its main characteristics are the ability to handle incomplete data and the flexibility to model complex relationships. Latent variable models are particularly useful in situations where the observed variables are the result of deeper, unobservable processes, such as attitudes, skills, or psychological traits. By providing a framework to understand how latent variables affect observed ones, these models enable researchers and analysts to make more accurate inferences and predictions, facilitating informed decision-making across various disciplines, including social sciences, finance, and health research.
