This course will introduce students to Bayesian data analysis. After a thorough review of fundamental concepts in statistics and probability, an introduction will be given to the fundamentals of the Bayesian approach, including a look at how computer simulation can be used to solve statistical problems. Students will learn how to use the brms package in the R statistical software environment to carry out Bayesian analyses of data commonly seen in health sciences. Bayesian methods will be covered for binary, continuous, and count outcomes in one and two samples, for logistic, linear, and Poisson regression, and for meta-analysis.

Objectives: by the end of this course, students will: 1) Understand what is meant by a "Bayesian Analysis" and how it differs from a typical analysis under the frequentist framework. 2) Understand the role and importance of Markov Chain Monte Carlo in modern Bayesian methods. 3) Understand how modern Bayesian models are fitted. 4) Be able to fit Bayesian models to common types of study designs and data types. 5) Know what aspects of the Bayesian analysis are an essential part of a statistical report. 6) Have worked through some case studies (in lectures, tutorials, and as part of assignments). 7) Have developed expertise in using the brms program within the R environment.