STAT 400: Bayesian Statistics

Number of Sections: 1 | Day and Time: T & TH (13:30-15:10 ICT)

Course Description

This course is an introduction to Bayesian statistics with an emphasis on practical applications to inference. Students will learn both how set up and analyze problems in the Bayesian framework. The course will teach students to use R and JAGS software for modelling  well as the Markov Chains Monte Carlo method (MCMC) for computation. The main topics to be covered include: Bayes’ theorem, prior and posterior distributions, inference for discrete and continuous random variables, hypothesis testing and model selection and linear regression. The course will consist of lectures and  in class computer sessions will be devoted to doing the modelling and estimation. Participation in these online working sessions is required.

Prerequisites:

MATH 100: Introduction to Probability

STAT 100: Introduction to Statistics

CS 251: Statistical Programming with R (preferred)