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 ) | $1,103.10 |
| 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
- Teaching Arrangements
This paper is taught via a combination of lectures, and hands-on practicals.
- Textbooks
To be determined.
- 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
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 | 0.1500 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- DATA 201 and STAT 270)
- Restriction
- STAT 423
- Schedule C
- Arts and Music, Science
- Notes
- DATA 201 prerequisite may be waived for students with suitable programming experience.
- Contact
- Teaching staff
- Teaching Arrangements
This paper is taught via a combination of lectures, and hands-on practicals.
- Textbooks
To be determined.
- 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