Latent and observable variables: Difference between revisions

Revision as of 07:51, 17 February 2022

Revision as of 07:52, 17 February 2022

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In [[statistics]], '''latent variables''' (from [[Latin]]: [[present participle]] of ''lateo'' (“lie hidden”), as opposed to [[observable variable]]s) are [[Variable (mathematics)|variables]] that are not directly observed but are rather [[Statistical inference|inferred]] (through a [[mathematical model]]) from other variables that are observed (directly measured). Mathematical models that aim to explain observed variables in terms of latent variables are called [[latent variable model]]s. Latent variable models are used in disciplines, including [[psychology]], [[demography]], [[economics]], [[engineering]], [[medicine]], [[physics]], [[machine learning]]/[[artificial intelligence]], [[bioinformatics]], [[chemometrics]], [[natural language processing]], [[econometrics]], [[management]] and the [[social sciences]].

Latent variables may correspond to aspects of physical reality. These could in principle be measured, but may not be for practical reasons. 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. The terms ''hypothetical variables'' or ''hypothetical constructs'' may be used in these situations.