Latent and observable variables: Difference between revisions

Revision as of 00:57, 8 April 2007

Revision as of 01:22, 8 April 2007

Line 1:

{{Merge|latent variable model|date=February 2007}}

In [[statistics]], '''Latent variables''' (as opposed to [[observable variable]]s), are [[variable]]s that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed and directly measured. They are also calledsometimes known as '''hidden variables''', '''model parameters''', '''hypothetical variables''' or '''hypothetical constructs'''. The use of latent variables is common in [[social science]]s, [[robotics]], and to an extent [[economics]], but the exact definition of a latent variablesvariable varies in these domainsfields. Examples of latent variables from the field of [[economics]] include [[quality of life]], business confidence, morale, happiness and conservatism.: [[Johnthese Fare all variables which cannot be measured directly. MacGregor]]However, atgiven [[McMasteran University]]economic pioneeredmodel theirlinking usethese inlatent [[chemicalvariables engineering]]to andother, observable variables (such as [[controlGross systemsdomestic product|GDP]]), bythe usingvalues themof asthe controlledlatent variables incan [[modelbe predictiveinferred control]]from measurements of the observable variables.

[[John F. MacGregor]] at [[McMaster University]] pioneered the use of latent variable methods 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 [[Dimensionality reduction|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.

One advantage of using latent variables is that it [[Dimensionality reduction|reduces the dimensionality]] of data. A large number of observable variables can be aggregated in a model 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 modeledmodelled world.

==See also==

Line 12 ⟶ 14:

[[Category:Econometrics]]

[[Category:Dimension]]

[[Category:Statistics]]

{{econometrics-stub}}