Introduction to Bayesian methods with an emphasis on data analysis. Topics include prior choice, posterior assessment, hierarchical modelling and model fitting using R, JAGS and other freely available software.
| Paper title | Bayesian Data Analysis |
|---|---|
| Paper code | STAT371 |
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees (NZD) | $955.05 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 260 and (STAT 261 or STAT 270)
- Restriction
- STAT 423
- Schedule C
- Arts and Music, Science
- Contact
- Teaching staff
Dr. Peter Dillingham
Associate Professor Matthew Schofield
- Textbooks
To be determined
- Course outline
- Graduate Attributes Emphasised
- Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship,
Communication, Critical thinking, Environmental literacy, Information literacy, Research,
Self-motivation, Teamwork.
View more information about Otago's graduate attributes. - Learning Outcomes
On successful completion of the paper, students will be able to:
- Understand the difference between Bayesian and frequentist statistics.
- Fit and interpret basic statistical models using Bayesian inference.
- Use modern software for Bayesian data analysis.
- Understand the role of prior distributions.
- Assess the fit of Bayesian models.
