Overview
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.
About this paper
| Paper title | Bayesian Data Analysis |
|---|---|
| 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.
Timetable
Overview
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.
About this paper
| Paper title | Bayesian Data Analysis |
|---|---|
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $981.75 |
| 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
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