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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 Metropolis-Hastings algorithm and the Glauber algorithm for sampling from the Boltzmann distribution. It explains the iterative rules and updates for the Curie-Weiss model, focusing on the Metropolis-Hastings algorithm's recipe and the Glauber algorithm's differential equations. The lecture also delves into the optimization of the code for computational efficiency and the analytical differential equations for the average magnetization. Through exercises and corrections, the lecture demonstrates the convergence to equilibrium configurations and the impact of finite size effects on the dynamics.