Search results

In statistics, latent variables (from Latin: present participle of lateo, “lie hidden”) are variables that can only be inferred indirectly through a mathematical...

A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) to a set of latent...

some latent variables (variables thought to exist but which can't be directly observed). Additional causal connections link those latent variables to observed...

formulation combines the two-way latent variable formulation above with the original formulation higher up without latent variables, and in the process provides...

closer to one another. Position within the latent space can be viewed as being defined by a set of latent variables that emerge from the resemblances from...

wij{\displaystyle w_{ij}} are the only observable variables, and the other variables are latent variables. As proposed in the original paper, a sparse Dirichlet...

the variables are independent. It is called a latent class model because the class to which each data point belongs is unobserved, or latent. Latent class...

explanatory variables and the output. In economics, binary regressions are used to model binary choice. Binary regression models can be interpreted as latent variable...

statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships...

parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E)...

model (HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as X {\displaystyle X} ). An HMM...

searches for such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors...

The value of the actual variable Y i {\displaystyle Y_{i}} is then determined in a non-random fashion from these latent variables (i.e. the randomness has...

Y} is the p x 1 vector of observed random variables, ξ{\displaystyle \xi } are the unobserved latent variables and Λ{\displaystyle \Lambda } is a p x k...

suspects that the error terms of any two variables are dependent (e.g. the two variables have an unobserved or latent common cause) then a bidirected arc is...

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...

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between...