7.7 Non-finite mixture model
For some problems, such as trying to cluster handwritten digits, it is easy to justify the number of groups we expect to find in the data. For other problems, we can have good guesses; for example, we may know that our sample of Iris flowers was taken from a region where only three species of Iris grow, thus using three components is a reasonable starting point. When we are not that sure about the number of components, we can use model selection to help us choose the number of groups. Nevertheless, for other problems, choosing the number of groups a priori can be a shortcoming, or we may instead be interested in estimating this number directly from the data. A Bayesian solution for this type of problem is related to the Dirichlet process.
7.7.1 Dirichlet process
All the models that we have seen so far have been parametric models, meaning models with a fixed number of parameters that we are interested in estimating, like a fixed number of clusters. We...
