Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.
Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.
Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.
