Bayesian 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
data analysis.
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.
Instructor: Harrison Quick, PhD, assistant professor, Dornsife School of Public Health, Drexel University.
