Introduction to Bayesian Statistics

Lecture, three hours; discussion, one hour. Enforced requisites: course 100B, Mathematics 32B. Designed for juniors/seniors. Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications, and computational issues. Topics include Stein paradox, nonparametric Bayes, and statistical learning. Examples of applications vary according to interests of students. Concurrently scheduled with course C236. P/NP or letter grading.

Review Summary

Clarity
6.7 / 10
Organization
10.0 / 10
Time
5-10 hrs/week
Overall
8.3 / 10

Reviews

    Quarter Taken: Fall 2023 In-Person
    Grade: A+

    Professor deeply cares about his students and their learning. Numerous lectures were used to solidify understanding of course material.

    Quarter Taken: Fall 2023 In-Person
    Grade: A

    Goated professor. Presented supplemental videos to help us understand the concepts. Super flexible regarding the deadlines of homework. No midterms. Take home final. However, I want to say that the concept can get tough when you try to understand them fully.

Course

Instructor
Yingnian Wu
Previously taught
23F

Grading Information

  • No group projects

  • Attendance not required

  • No midterms

  • Finals week final

  • 0% recommend the textbook

Previous Grades

Grade distributions not available.