Dirichlet Process

A Dirichlet process is a statistical method used in machine learning and Bayesian statistics to model data that may belong to an unknown number of groups or categories. It allows for flexible clustering, meaning that as new data comes in, the process can create new groups without having to specify how many beforehand. Think of it like an ever-expanding buffet where each new dish represents new data points, and existing dishes can gather more guests, adapting naturally to what’s presented without a rigid structure. This flexibility is valuable for understanding complex datasets.