Variational Inference for Functional Data Clustering via Dirichlet Process Mixtures with Correlated Errors

We propose a Bayesian model-based approach for clustering functional data with an unknown number of clusters and temporally correlated observations. Cluster-specific mean functions are represented using B-spline basis expansions, while within-curve dependence is modeled through an Ornstein--Uhlenbec