BIOSTAT 202C

Theory of Bayesian Statistics

Description: Lecture, three hours; discussion, one hour. Requisites: courses 200A, 200B, 202A, 202B, or equivalent, or consent of instructor. Mathematical underpinnings of Bayesian approach to statistical inference; closed form computations; computation; hierarchical models; model selection; hypothesis testing; prior specification; comparative inference; nonparametric methods. S/U or letter grading.

Units: 4.0
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Jason Clague

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Andrew Holbrook

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Benjamin Rogers

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Donatello Telesca

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Robert Weiss

Overall Rating 1.0
Easiness 5.0/ 5
Clarity 2.0/ 5
Workload 4.0/ 5
Helpfulness 1.0/ 5
Most Helpful Review
Spring 2024 - Robert was not an effective instructor. Lectures were often unclear and lacked organization, and the presentation slides were difficult to follow. Grading appeared inconsistent across students, and assignment expectations were not clearly communicated. Overall, I would not recommend this instructor for a graduate-level biostatistics course.
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