Not all the parameters (e.g., the assignment parameters) for a Gaussian mixture model are smoothly differentiable, thus can not be fit with gradient descent.

Other use cases for expectation–maximization (EM) algorithm are:

• Clustering
• Latent variable estimation
• Missing data estimation

Not all the parameters (e.g., the assignment parameters) for a Gaussian mixture model are smoothly differentiable, thus can not be fit with gradient descent.

Other use cases for the expectation–maximization (EM) algorithm are:

• Clustering
• Latent variable estimation
• Missing data estimation