BIOL 4069
Applied Bayesian Statistics
Lec 01 selected
This course, which uses Richard McElreath’s popular ‘Statistical Rethinking’ book as a foundation, provides a broad introduction to applied Bayesian models using R or Python. Examples are drawn from Biology and Social Science, in contexts designed to be widely applicable to analysis of observational data across disciplines.
Sections
Lecture
Lec 01
Mon, Wed, Fri 8:35-9:25
From the syllabus
From the Fall 2025 syllabus by Aaron MacNeil and Arun Oakley-Cogan. Open the PDF
Grading
30%Weekly homework (6)5% each; every second Friday
20%End-of-term assignment
20%MidtermOctober 20
30%Final examDuring the final exam period
Textbook
- Statistical Rethinking, 2nd edition, Richard McElreath
Course rules
- Late work
- Late homework and assignments are not accepted, except for case-by-case exceptions such as illness or bereavement.
- Attendance
- Class attendance is strongly recommended.
- Generative AI
- Use is discouraged; if used, it must be clearly indicated.
- Collaboration
- Collaboration is encouraged, but submitted work must be your own and borrowed code must be cited in script comments.
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