Bayesian Analytics in Practice — Professional Development Continuing Education Course
The Bayesian paradigm provides a natural and practical way for building complex analytical models by expressing the joint model through a sequence of simpler conditional models, making it useful for various hierarchical data structures. This course will first introduce general notions of Bayesian methods via hierarchical models, and then expand the topic with the more realistic and complex models which have recently emerged as a result of current Machine Learning literature. These models will be illustrated through practical applications to various real case studies avoiding much of the theoretical underpinnings. Participants with basic knowledge of probability theory and statistical inferential framework will find the course useful in expanding their toolkit with the advanced use of Bayesian analytical methods. Popular topics such as prior sensitivity analysis, model comparisons, and uncertainty quantification for machine learning methods will be covered. The concepts and methods discussed will be demonstrated using primarily R and SAS software illustrations developed by the presenters, but methodologies presented can also be carried by other software (e.g., Python). Group activities will be encouraged, allowing participants to have a hands-on experience. Lecture materials used for the workshop will be distributed electronically and thus can also be offered virtually.
Instructor(s): Sujit Ghosh, North Carolina State University; Amy Shi, AstraZeneca