Belief Propagation on Graphs

This lecture introduces belief propagation on graphs, focusing on fully connected graphs and trees. The instructor explains the normalization process, the free energy concept, and the use of auxiliary partition functions. The lecture covers the recursive relations for messages, the transformation to chi and psi variables, and the application of belief propagation on random graphs. The instructor emphasizes the importance of normalizing messages and demonstrates how to iteratively compute the partition function on trees. The lecture concludes by discussing the implications of belief propagation on graphs and the computation of the partition function.