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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
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.