Advanced Bayesian Analysis

Catalog description

Prerequisites: Introduction to Bayesian Analysis, Applied Survival Analysis A combination of advanced Bayesian principles, tools and methods. Emphasis is on modern computations for parametric and nonparametric models with a deeper dive into NIMBLE/Stan and state-of-the-art sampling techniques, convergence diagnostics, goodness-of-fit, etc. Topics include Bayes factors, HPD regions, conjugate/non-informative priors, the generalized linear models, hierarchical/mixed models, multivariate data, restricted parameter spaces/time-to-event analysis with censored data, Dirichlet Process Mixtures, Gaussian Processes, Bayesian Additive Regression Trees (BART), advanced computational techniques like stochastic gradient descent and illustrative examples of Bayesian analyses for complex biomedical data.