Listed in: Mathematics and Statistics, as STAT-363
Kevin Donges (Section 01)
Statistical inference using what are called frequentist methods, where only the data are random, has long dominated the manner in which data are analyzed. The rise of computing power this century has unlocked Bayesian inference, a technique that blends prior knowledge with data, as an increasingly popular and powerful alternative approach. This course will explore the theory behind and application of Bayesian inference including situations where Markov Chain Monte Carlo (MCMC) simulation is employed.
Omitted 2023-24. Professor Donges
How to handle overenrollment: Priority for Statistics majors
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, problem sets, quizzes or exams, group work, use of computational software, reading research articles, projects, oral presentations
Course Materials