Latent and observable variables

[2] Such latent variable models are used in many disciplines, including engineering, medicine, ecology, physics, machine learning/artificial intelligence, natural language processing, bioinformatics, chemometrics, demography, economics, management, political science, psychology and the social sciences.

For we mean not the measures, symptoms, or degrees of any process which can be exhibited in the bodies themselves, but simply a continued process, which, for the most part, escapes the observation of the senses.In this situation, the term hidden variables is commonly used (reflecting the fact that the variables are meaningful, but not observable).

Other latent variables correspond to abstract concepts, like categories, behavioral or mental states, or data structures.

Many observable variables can be aggregated in a model to represent an underlying concept, making it easier to understand the data.

Observable variables to measure quality of life include wealth, employment, environment, physical and mental health, education, recreation and leisure time, and social belonging.

Estimation of a mean height curve (black) for boys from the Berkeley Growth Study with and without warping. The warping is based on latent variables that maps age to a synchronized biological age using a nonlinear mixed-effects model . [ 4 ]