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    Methodological and computational aspects of Bayesian statistics, with applications.

    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 ) $1,240.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    STAT 401 or (STAT 260 and STAT 270 and STAT 310), or equivalent (contact department for further information)
    STAT 371

    Suitable for graduates and professionals interested in advanced modelling with some experience in R programming and knowledge of probability theory.


    Dr Peter Dillingham

    Teaching staff

    Dr Peter Dillingham

    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.


    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:

    1. Explain key concepts in Bayesian statistics such as the link between the likelihood, prior and posterior distributions.
    2. Understand the relationship between Bayesian and frequentist approaches.
    3. Understand sufficient theory to find analytical solutions to standard problems (note: ‘standard’ problems in Bayesian statistics require advanced statistical knowledge).
    4. 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.
    5. Be able to implement their own Markov chain Monte Carlo samplers in R.
    6. Communicate results to others and understand the ethical and scientific importance of reproducible research.
    7. Independently develop advanced hierarchical statistical models linked to a scientific study, create and perform an appropriate Bayesian analysis.


    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system


    Stream Days Times Weeks
    A1 Tuesday 09:00-09:50 29-35, 37-42
    Wednesday 09:00-09:50 29-35, 37-42
    Thursday 09:00-09:50 29-35, 37-42


    Stream Days Times Weeks
    A1 Thursday 15:00-16:50 29-35, 37-42
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