methods combine information from various sources and are increasingly
used in biomedical and public health settings to accommodate complex
data and produce readily interpretable output. This course will
introduce students to Bayesian methods, emphasizing the basic
methodological framework, real-world applications, and practical
computing. Special consideration will be given to methods for spatial
After completing this course, participants will be able to:
Understand the fundamentals of Bayesian inference and the differences between Bayesian and frequentist (classical) methods.
Formulate research questions and develop Bayesian approaches to address these questions.
Be familiar with the available software for implementing Bayesian methods.
Understand advanced Bayesian methods used in the scientific literature.
Prerequisite knowledge: Basic understanding of linear
regression and "generalized linear models" (e.g., logistic and Poisson
regression) is required for this course. Statistical programming
experience is recommended but not required.
Technical requirements: R and WinBUGS are required for
the course. The free software is available for participants to install
on their computers.