An introduction to the Bayesian approach to statistical inference, its theoretical foundations and comparison to classical methods. Topics include parameter estimation, testing, prediction and computational methods (Markov Chain Monte Carlo simulation). Emphasis on concepts, methods and data analysis. Extensive use of the R programming language and examples from the social, biological and physical sciences to illustrate concepts.
4
UnitsOptional
Grading1, 2
PasstimeNone
Level LimitLetters and science
CollegeThird class with Wainwright, super chill, lecture not required but helpful as he explains nuance and is responsive to lecture questions. Go to office hours with actual questions and you should be set for exams. Homework can be done with a partner which makes them super manageable.
Brian can be very charismatic in his lectures, and explains the content well enough. The course contains a lot of difficult content to cover but his grading scale is very lenient, however the exams (particularly the midterm) were very nitpicky on the particular content used, and covered 50% of the final grade; section attendance was also required.
No curve at all. The exams are straightforward but covers an insane amount of content, so it's really hard for you to memorize every detail clearly. However, those things that you fail to notice are highly likely to appear on the exam. Afterall it's a tough course and the professor is not good.
He taught Bayesian Statistics very well and used lots of real world examples which helps to remind you of the "why" we do any of the statistics. He explained things very well with very helpful visuals which made it easy to understand the theory. Attendance isn't mandatory but I highly recommend going to the lectures. Great course and professor.
great professor
Brian was an okay instructor. The material is fairly complicated and he was able to break it down somewhat simply. 50% of your grade is final and midterm. Four HWs were challenging and you could work on them with a partner. Midterm was incredibly difficult and the final was take home and much fairer.