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
Collegegreat 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.
Lectures were very informative but there definitely wasn't enough time which is why I recommend doing practice problems from the textbook. To study for exams, I would recommend doing practice problems instead of just doing the homework and attending lectures/section.
His lectures are useless. Half the grade is free, midterm and final are hard.
Prof Wainwright is a great guy, and his class was really interesting. Took 120A as an elective, and it was fairly hard, but if you keep up with TA material and study with his resources (and CLAS) you will do fine. He helped me out when I had a scheduling conflict and really does care about student success. You will be extremely prepared for 120B!
Very good professor. HW is slightly challenging, but is 50 percent of grade and is based on completion which is great. Both the midterm and the final are very fair, as long as you understand the material. The only slight downside is that his lectures are slightly boring. Also, the PSTAT CLAS 120A made my Jeremy Li is incredibly helpful.