A latent variable, is a variable in a Bayesian network that is neither an input or output variable, and hence has no data associated with it during parameter learning.

Latent variables can either be discrete, or continuous.

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Latent variables are also known as hidden variables. |

# Discrete latent variables

An example of a discrete latent variable is the **Cluster** node in the mixture model network shown below.

Instead of a single multivariate Gaussian, we have made the model more flexible by allowing multiple multivariate Gaussians, as shown in the image below.

# Continuous latent variables

An example of a continuous latent variable is the **x** node in the Kalman filter network shown below.

The continuous latent variable is used in this case to represent an unobservable process.

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Models such as this are often used to track missiles. |

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Continuous latent variables can also be used for dimensionality reduction (e.g. probabilistic PCA). |