Bi 103b
Statistical Inference in the Biological Sciences
This course introduces students to statistical modeling and inference, primarily taking a Bayesian approach. Topics include generative modeling, parameter estimation, model comparison, hierarchical modeling, Markov chain Monte Carlo, graphical display of inference results, and principled workflows. Other topics may also be included. All techniques are applied to real biological data sets in class and in homework.
Topics
- generative modeling
- parameter estimation
- model comparison
- hierarchical modeling
- Markov chain Monte Carlo
- graphical display of inference results
- and principled workflows
Topics explicitly listed in official catalogue description
Prerequisites & course information
Prerequisites: BE/Bi 103 a or equivalent; Ma 1 abc and Ma 3, or Bi/CNS/NB 195, or equivalent; or instructor's permission.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Statistical Inference in the Biological Sciences · 9 units (1-3-5) | second term