Bayesian Statistics

This course will introduce the Bayesian approach to data analysis (including choice of prior distributions and calculation of posterior distributions) with an emphasis on practical applications. Topics to be discussed include: Bayes’ Theorem; prior distributions; inferences for discrete random variables and binomial proportion; inferences for continuous random variable and normal means; linear regression; analysis of variance; MCMC/Gibbs sampler; and model evaluation/comparison.
Prerequisites: EN 102, COM 101, MA 331