Measurement invariance is relevant in the context of latent variables.

Such a system is then called an observable (latent variable) system.

The observed items are conditionally independent of each other given an individual score on the latent variable(s).

The unknown quantity may be a parameter or latent variable.

These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

Typically these models involve latent variables in addition to unknown parameters and known data observations.

In statistical models with latent variables, this usually is not possible.

In this case, each latent variable has only a single dependent child word, so only one such term appears.

We would then use three latent variables, one for each choice.

The unobserved variable x* may be called the latent or true variable.