STAT 206: Applied Bayesian
University of California, Santa Cruz
Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, etc.), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models). (Formerly AMS 206.)
Average GPA: 3.76
Grade distribution records: 501 students across 5 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 260 | 51.9% |
| A | 128 | 25.5% |
| A- | 16 | 3.2% |
| B+ | 17 | 3.4% |
| B | 5 | 1.0% |
| B- | 6 | 1.2% |
| C+ | 3 | 0.6% |
| C | 7 | 1.4% |
| F | 15 | 3.0% |
| P | 7 | 1.4% |
| NP | 2 | 0.4% |
| S | 28 | 5.6% |
| W | 7 | 1.4% |
Based on 501 student grade records across 5 terms and 1 professor.
Instructors
- David Draper 501 students, Average GPA 3.76