Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.