Markov Chain Monte Carlo

This lecture covers the Markov Chain Monte Carlo method, starting with a collaborative exercise on ergodic chains and stationary distributions. The lecture explains the concept of apothetic chains, the addition of loops to improve convergence, and the Metropolis algorithm for finding the minimum of a function. It delves into the complexities of calculating probabilities, the importance of chain convergence, and the role of temperature in exploring state spaces. The instructor demonstrates how adjusting the temperature parameter gradually organizes the exploration of states to find the optimal solution.