Bayesian Networks: Factorization and Sampling

This lecture covers the concept of Bayesian Networks, focusing on factorization and sampling methods. It explains how to sample from a distribution using Metropolis Hastings Algorithm and Variable Elimination, and how to factorize a joint distribution into conditional independencies using Directed Acyclic Graphs (DAGs). The instructor discusses the importance of active directed paths and conditional independence relationships in Bayesian Networks.