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.
Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Focuses on large-scale inference for detecting QTL hotspots in sparse regression models, emphasizing the need to use genomics to understand variation in phenotypes and disease susceptibility.
