Markov Chains: Simulation and Optimization

This lecture covers the concepts of Markov chains, Metropolis-Hastings algorithm, and simulation techniques for optimization. It explains the properties of Markov processes, stationary distributions, and how to simulate random variables using Markov chains. Examples illustrate the application of these concepts in modeling degradation processes and calculating stationary probabilities. The instructor emphasizes the importance of ergodicity in Markov chains for simulating random variables efficiently.