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    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.

    Teaching staff

    Dr Peter Dillingham


    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:

    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


    Not offered in 2024

    Teaching method
    This paper is taught On Campus
    Learning management system
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