Markov Chains and Applications

This lecture covers the concept of Markov chains and their applications in algorithms. The instructor explains the motivation behind using Markov chains, the problem of user impatience, and issues related to mixing. The lecture also delves into the exact simulation of Markov Chain Monte Carlo (MCMC) and the challenges faced in generating faithful samples. Various techniques like random mapping representation and coupling from the past are discussed, along with their significance in simulating Markov chains accurately.