About this paper
| Paper title | Special Topic |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Not offered in 2024 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,482.46 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Contact
- Teaching staff
- Textbooks
No textbook required.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Ethics, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
On successful completion of the paper students will be able to:
- Explain key concepts in Bayesian statistics such as the link between the likelihood, prior and posterior distributions
- Understand the relationship between Bayesian and frequentist approaches
- Understand sufficient theory to find analytical solutions to standard problems (note: ‘standard’ problems in Bayesian statistics require advanced statistical knowledge)
- Be able to independently use R with JAGS or stan to complete Bayesian statistical analyses, including the ability to correctly format and manipulate input different data types, run analyses and diagnostics, and interpret and plot results
- Be able to implement their own Markov chain Monte Carlo samplers in R
- Communicate results to others and understand the ethical and scientific importance of reproducible research
- Independently develop advanced hierarchical statistical models linked to a scientific study, create and perform an appropriate Bayesian analysis
Timetable
About this paper
| Paper title | Special Topic |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Not offered in 2025 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2025 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Contact
- Teaching staff
- Textbooks
No textbook required.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Ethics, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
On successful completion of the paper students will be able to:
- Explain key concepts in Bayesian statistics such as the link between the likelihood, prior and posterior distributions
- Understand the relationship between Bayesian and frequentist approaches
- Understand sufficient theory to find analytical solutions to standard problems (note: ‘standard’ problems in Bayesian statistics require advanced statistical knowledge)
- Be able to independently use R with JAGS or stan to complete Bayesian statistical analyses, including the ability to correctly format and manipulate input different data types, run analyses and diagnostics, and interpret and plot results
- Be able to implement their own Markov chain Monte Carlo samplers in R
- Communicate results to others and understand the ethical and scientific importance of reproducible research
- Independently develop advanced hierarchical statistical models linked to a scientific study, create and perform an appropriate Bayesian analysis