Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website
|Paper title||Special Topic|
|Teaching period||Not offered in 2022 (On campus)|
|Domestic Tuition Fees (NZD)||$1,403.61|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- Teaching staff
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 the student 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.