Bayesian Interference in Data Science

Catalog description

This course introduces the Bayesian approach. It involves the concept of probability and the analysis of data which focuses on the principles of data analysis and computer-intensive, modern statistical modeling. Topics include Bayesian inference, prior and posterior distributions, regression modeling, hierarchical models, model checking and selection, missing data, and stochastic simulation by Markov Chain Monte Carlo including Gibbs sampling and Metropolis algorithms. The course will apply Bayesian methods to practical problems, by building models from the prior probabilities to the posterior distribution with statistical packages.