# Gaussian Mixture Models

Gaussian mixture models assume that the input is generated by a mixture of a
finite number of Gaussian distributions with unknown parameters.

![gmm](gmm.png)

Typically an EM (Expectation Maximization) algorithm is used to estimate these
parameters, which include the mean, covariance and mixture ratios of the
underlying components.

We provide a distributed implementation for running the GMM algorithm. Options
are provided to let the user decide on which parameters to learn. The learned
covariance can be either full or diagonal.