Random Processes and Monte Carlo Simulation

This lecture covers the concept of random processes with given probabilities, illustrated through a hypothetical case of attending online lectures. It also delves into Monte Carlo simulation, demonstrating how to generate random processes with known probabilities. The Metropolis algorithm is explained, detailing the process of accepting or rejecting new configurations based on energy levels. Stochastic matrices and detailed balance in Monte Carlo sampling are discussed, emphasizing the importance of fulfilling specific conditions. The lecture concludes with insights on trial moves, highlighting the ideal properties for efficient and accurate simulations.