Bayesian modelling with an emphasis on scientific data analysis and computing. Topics include Markov chain Monte Carlo, prior choice, posterior assessment, hierarchical modelling, model fitting, and model selection using R, JAGS and other freely available software.
About this paper
| Paper title | Bayesian Modelling |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,393.95 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 401 or (STAT 260 and STAT 270 and STAT 310), or equivalent (contact department for further information)
- Restriction
- STAT 371
- Eligibility
Suitable for graduates and professionals interested in advanced modelling with some experience in R programming and knowledge of probability theory.
- Contact
- Teaching staff
- Paper Structure
- Theory of Bayesian inference.
- Markov chain Monte Carlo methods.
- Modelling using JAGS and Stan.
- Advanced Scientific modelling.
- Teaching Arrangements
This paper is taught via a combination of interactive lectures, hands-on practicals, and project meetings.
- Textbooks
To be advised.
- Graduate Attributes Emphasised
interdisciplinary perspective, lifelong learning, and scholarship; and attributes sought by employers communication, critical thinking; ethics; information literacy; research, and self-motivation
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will:
- 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.
Bayesian modelling with an emphasis on scientific data analysis and computing. Topics include Markov chain Monte Carlo, prior choice, posterior assessment, hierarchical modelling, model fitting, and model selection using R, JAGS and other freely available software.
About this paper
| Paper title | Bayesian Modelling |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 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
- STAT 401 or (STAT 260 and STAT 270 and STAT 310), or equivalent (contact department for further information)
- Restriction
- STAT 371
- Eligibility
Suitable for graduates and professionals interested in advanced modelling with some experience in R programming and knowledge of probability theory.
- Contact
- Teaching staff
- Paper Structure
- Theory of Bayesian inference.
- Markov chain Monte Carlo methods.
- Modelling using JAGS and Stan.
- Advanced Scientific modelling.
- Teaching Arrangements
This paper is taught via a combination of interactive lectures, hands-on practicals and project meetings.
- Textbooks
To be advised.
- 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
Students who successfully complete the paper will:
- 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.