Latent and observable variables: Difference between revisions

Revision as of 00:12, 8 December 2005

Revision as of 16:56, 25 January 2006

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'''Latent variables''', as opposed to [[observable variable]]s, are those [[variable]]s that cannot be directly observed but are rather inferred from other variables that can be observed and directly measured. Examples of latent variables include [[quality of life]], business confidence, morale, happiness, conservatism. Latent variables are also called as hidden variables, model parameters, hypothetical variables or hypothetical constructs. The use of latent variables is common in [[social sciencesscience]]s, [[robotics]], and to an extent in the economics ___domain.; Thebut the exact definition of latent variables varies in these different domains.

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 human beings to understand and assimilate information. At the same time, the latent variables are the link between observable ("sub-symbolic") data in the real world, and symbolic data in the modeled world.

[[Category:Social sciences]]

[[Category:Bayesian networks]]

[[Category:Economics]]