## ⓘ Latent variable model

A latent variable model is a statistical model that relates a set of observable variables to a set of latent variables.

It is assumed that the responses on the indicators or manifest variables are the result of an individuals position on the latent variables, and that the manifest variables have nothing in common after controlling for the latent variable local independence.

Different types of the latent variable model can be grouped according to whether the manifest and latent variables are categorical or continuous:

The Rasch model represents the simplest form of item response theory. Mixture models are central to latent profile analysis.

In factor analysis and latent trait analysis the latent variables are treated as continuous normally distributed variables, and in latent profile analysis and latent class analysis as from a multinomial distribution. The manifest variables in factor analysis and latent profile analysis are continuous and in most cases, their conditional distribution given the latent variables is assumed to be normal. In latent trait analysis and latent class analysis, the manifest variables are discrete. These variables could be dichotomous, ordinal or nominal variables. Their conditional distributions are assumed to be binomial or multinomial.

Because the distribution of a continuous latent variable can be approximated by a discrete distribution, the distinction between continuous and discrete variables turns out not to be fundamental at all. Therefore, there may be a psychometrical latent variable, but not a psychological psychometric variable.

Recently DSDs and Latent Variable modeling were applied for the first time to the optimization of an extraction procedure in order to analyze target compounds present in wine samples. Latent Variable modeling can be a relevant tool for the optimization of analytical techniques, contributing to the implementation of rigorous, systematic and more efficient optimization protocols.

- mathematical model from other variables that are observed directly measured Mathematical models that aim to explain observed variables in terms of latent variables
- a latent class model LCM relates a set of observed usually discrete multivariate variables to a set of latent variables It is a type of latent variable
- latent variable models together with a measurement model or as probabilistic models directly modeling the probability. The latent variable interpretation
- low - dimensional representation of the observed variables in terms of their affinity to certain hidden variables just as in latent semantic analysis, from which PLSA
- discriminative probabilistic latent variable models DPLVM are a type of CRFs for sequence tagging tasks. They are latent variable models that are trained discriminatively
- measured. Observable physics Observability in control theory Latent variable model Dodge, Y. 2003 The Oxford Dictionary of Statistical Terms, OUP
- that defines latent variables using one or more observed variables and a structural model that imputes relationships between latent variables The links
- called the latent or true variable It may be regarded either as an unknown constant in which case the model is called a functional model or as a random
- latent tree analysis HLTA is an alternative to LDA, which models word co - occurrence using a tree of latent variables and the states of the latent variables
- In natural language processing, the latent Dirichlet allocation LDA is a generative statistical model that allows sets of observations to be explained

Doubly stochastic model |

Dynamic unobserved effects model |

Factor regression model |

First-difference estimator |

Nuisance variable |

Partial least squares regression |

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