Structure Learning: Chow-Liu Algorithm

This lecture covers the Chow-Liu Algorithm for structure learning, which aims to learn the graph encoding a distribution from samples. It explains how the algorithm approximates the best tree structure based on joint probabilities, minimizing the K-L divergence between distributions. The lecture delves into the theoretical foundations and practical applications of the algorithm, emphasizing the importance of spanning trees and maximizing information gain. Additionally, it discusses the projection of distributions onto maximum weight spanning trees to minimize divergence. The presentation concludes with a detailed analysis of the algorithm's optimization process and its implications in probabilistic graphical models.