Explore nonparametric Bayesian methods in this lecture from the Foundations of Machine Learning Boot Camp. Delve into probabilistic models for graphs, examining both traditional node-based approaches and newer edge-based techniques. Learn about exchangeability in various contexts, including node exchangeability, edge exchangeability, and the Aldous-Hoover theorem. Discover the concept of graph paintboxes and their role in edge-exchangeable graphs. Investigate feature allocation and its connection to exchangeability through feature paintbox representations. Gain insights into proving sparsity in graph sequences and understand the current state of knowledge in nonparametric Bayesian methods for graph modeling.