 Dependent Dirichlet process

The Dependent Dirichlet Process (DDP) is a Bayesian nonparametric model used in machine learning and statistics to model data that exhibit some form of dependency among groups or clusters. It extends the Dirichlet Process (DP) by incorporating dependence structures between multiple processes. ### Key Concepts: 1. **Dirichlet Process (DP)**: - The DP is a stochastic process used as a prior distribution over probability measures.