STAT6300

1. Understand and explain how statistical inference differs between the classical (Frequentist) and Bayesian perspectives. 2. For a specified analysis task and data structure: select appropriate Bayesian modeling frameworks, specify appropriate prior distributions, construct likelihood functions, and derive the corresponding posterior distributions. 3. Conduct Bayesian inference and report it via graphical and numerical summaries of posterior distributions. 4. Understand Markov Chain Monte Carlo algorithms (integration and sampling) and implement direct sampling, Gibbs sampling, and the Metropolis-Hastings algorithm. 5. Be able to approach and use common computational tools supporting implementation of Bayesian data analysis: e.g. R, STAN, Bugs. 6. Be able to read and understand Bayesian data analysis as presented in the scientific literature.