Chapter 7. Probabilistic Mixture Models
We have seen an initial example of mixture models, namely the Gaussian mixture model, in which we had a finite number of Gaussians to represent a dataset. In this chapter, we will focus on more advanced examples of mixture models, going again from the Gaussian mixture model to the Latent Dirichlet Allocation. The reason for so many models is that we want to capture various aspects of the data that are not easily captured by a mixture of Gaussian.
In many cases, we will use the EM algorithm to find the parameters of the model from the data. Also, it appears that most of the mixture models can have intractable solutions and need solutions on approximate inferences.
The first type of model we will see is a mixture of simple distributions. The simple distribution can be a Gaussian, a Bernoulli, a Poisson, and so on. The principle is always the same but the applications are different. If Gaussian distributions are nice for capturing clouds of points...
