Dirichlet Process

The Dirichlet process (DP) is a Bayesian nonparametric prior distribution used to model the unknown number of components in a mixture model, enabling flexible clustering and density estimation. Current research focuses on extending DP-based models, such as Dirichlet process mixture models (DPMMs), to handle complex data structures (e.g., tensors, time series, graphs) and challenging learning scenarios (e.g., lifelong learning, long-tail recognition), often incorporating techniques like collapsed Gibbs sampling or variational inference for efficient computation. These advancements are improving the scalability and robustness of DP methods, leading to applications in diverse fields including image analysis, signal processing, and customer behavior modeling.