Latent and observable variables

In statistics, Latent variables (as opposed to observable variables), are variables that are not directly observed but are rather inferred from other variables that are observed and directly measured. They are also called hidden variables, model parameters, hypothetical variables or hypothetical constructs. The use of latent variables is common in social sciences, robotics, and to an extent economics, but the exact definition of latent variables varies in these domains. Examples from the field of economics include quality of life, business confidence, morale, happiness and conservatism. John F. MacGregor at McMaster University pioneered their use in chemical engineering and control systems by using them as controlled variables in model predictive control.

One advantage of using latent variables is that it reduces the dimensionality of data. A large number of observable variables can be aggregated to represent an underlying concept, making it easier for humans to understand the data. In this sense, they serve the same function as theories in general do in science. At the same time, latent variables link observable ("sub-symbolic") data in the real world, to symbolic data in the modeled world.

See also

• Latent variable model