Latent variables, as created by factor analytic methods, generally represent 'shared' variance, or the degree to which variables 'move' together.
Latent Variable. A latent variable is a variable that cannot be measured directly, but is hypothesized to underlie the observed variables. An example of a latent variable is a factor in factor analysis.
The components of executive function (as determined through previous latent variable analyses) can be loosely described as inhibition (the ability to resist habit), ...
An expectation-maximization (EM) algorithm is used in statistics for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables.
Invented by Geoff Hinton and Terry Sejnowski (1985), the Boltzmann machine was important because it was one of the first neural networks in which learning of latent variables (hidden units) was demonstrated.
PLS regression replaces the initial space of the (many) regressors by a low-dimensionality space spanned by a small number of variables called "factors", or "latent variables". Factors are built iteratively.
"Variational Bayes for 1-dimensional mixture models"
"Ensemble Learning for Hidden Markov Models"
"Inferring parameters and structure of latent variable models by variational Bayes"
"Bayesian parameter estimation via variational methods" ...
In statistical computing, an expectation-maximization (EM) algorithm is an algorithm for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. ...
 See also: Distribution, Neural network, Regression, Percept, Data mining
  
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