Latent variables refer to variables that cannot be directly observed. These variable are defined in terms of observable variables. In narrow sense, "latent variable" is seen/modeled as what generates the observed variables in an implied data generation process. Also called hidden or lurking variables.

Common applications using latent variable models:

  • Hidden Markov models
  • Factor analysis
  • Principal component analysis
  • Partial least squares regression
  • Latent semantic analysis and Probabilistic latent semantic analysis
  • EM algorithms

Bayesian methods include:

  • 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

In graphical models, latent variables are represented by ovals/circles and observed values are represented by squares/rectangles:

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See also

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