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|
|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.|
- STAT 260 and (STAT 261 or STAT 270)
- STAT 423
- Schedule C
- Arts and Music, Science
- Teaching staff
Dr. Peter Dillingham
Associate Professor Matthew Schofield
To be determined
- Course outline
- Graduate Attributes Emphasised
- Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship,
Communication, Critical thinking, Environmental literacy, Information literacy, Research,
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.