Bayesian Extremes: MCMC Analysis

This lecture covers the application of Markov chain Monte Carlo (MCMC) algorithms to Bayesian techniques for extreme value problems. It discusses the advantages of MCMC, such as including additional information through appropriate priors, ease of inference, and predictive inference development. The lecture also explores the importance of proposal distribution choice, convergence diagnostics, and the use of MCMC for Bayesian modeling. It emphasizes the need for proper prior information, expert knowledge elicitation, and the challenges in modeling uncertainty. The lecture concludes with a discussion on the use of graphs for model-checking and presenting conclusions.