Introduction to Bayesian Data Analysis . PSTAT 115
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.
Prerequisites: PSTAT 126 with a minimum grade of C.
4
UnitsOptional
Grading1, 2
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Level LimitLetters and science
CollegeThese majors only finms actsc stsds stsap stats
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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.
midterm was hard, but he curved a lot. Final was super essay, as far as i know a lot of people get full credits. However, his lecture was boring, but you have to go there since the explainations on the textbook were not clear. If you only read the textbook, it will be hard to get an A.
The first half of the quarter's content was covered really quickly and pretty confusing, but he slowed down a lot in the last half. Once he slowed down, I actually liked his lectures. The midterm was quite long so scores were very low, but he curved the whole class a lot at the end. The final was reasonable.
Uma Ravat
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