Markov Chains: PageRank Algorithm

This lecture covers the concept of Markov chains, focusing on the PageRank algorithm used by search engines to rank web pages. The instructor explains the theory behind PageRank, including the importance of irreducibility and periodicity in the transition matrix. The lecture delves into examples of transient and recurrent states within Markov chains, illustrating how the algorithm calculates the stationary distribution. Additionally, the instructor discusses the impact of adding random links to ensure ergodicity and convergence in the PageRank algorithm. The lecture concludes with insights into the evolution of PageRank and the challenges faced in scaling the algorithm for large-scale web applications.