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Luke Maurits (talk | contribs) Linked to some common algorithms/methods. Might do more work soon. |
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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 modelled world.
==Common methods for inferring latent variables==
* [[Factor analysis]]
* [[Principal component analysis]]
* [[Latent semantic analysis]] and [[Probabilistic latent semantic analysis]]
* [[EM algorithm]]
===Bayesian algorithms and methods===
[[Bayesian statistics]] is often used for inferring latent variables.
* [[Latent Dirichlet Allocation]]
* The [[Chinese Restaurant Process]] is often used to provide a prior distribution over assignments of objects to latent categories.
* The [[Indian Buffet Process]] is often used to provide a prior distribution over assignments of latent binary features to objects.
==Examples of latent variables==
===Psychology===
* The "[[Big Five personality traits]]" have been inferred using [[factor analysis]].
* extraversion <ref name="status"> Borsboom, Mellenbergh, van Heerden (2003) ''The Theoretical Status of Latent Variables'' Psychological Review Vol 110, No 2 http://rhowell.ba.ttu.edu/BorsboomLatentvars2003.pdf</ref>
* spatial ability <ref name="status"/>
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