Probabilistic Estimation in Spin Glass Card Game

This lecture covers the probabilistic estimation techniques used in the Spin Glass Card game, focusing on splitting a room into two groups with similar cards based on a given parameter Y. The instructor explains the Bays-optimal estimator, mean-squared-error, and how to minimize it. The lecture also delves into Monte Carlo Markov Chain algorithms and Metropolis-Hastings Monte Carlo methods. Detailed balance and equilibrium distributions are discussed, along with practical examples and computational strategies.