Overview

    Introduction to Bayesian methods with an emphasis on data analysis. Topics include prior choice, posterior assessment, hierarchical modelling and model fitting using R, JAGS and other freely available software.

    About this paper

    Paper title Bayesian Data Analysis
    Subject Statistics
    EFTS 0.15
    Points 18 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $955.05
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Prerequisite
    STAT 260 and (STAT 261 or STAT 270)
    Restriction
    STAT 423
    Schedule C
    Arts and Music, Science
    Contact

    peter.dillingham@otago.ac.nz

    Teaching staff

    Dr. Peter Dillingham

    Associate Professor Matthew Schofield

    Textbooks

    To be determined

    Course outline

    View the course outline for STAT371

    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

    On successful completion of the paper, students will be able to:

    • Understand the difference between Bayesian and frequentist statistics.
    • Fit and interpret basic statistical models using Bayesian inference.
    • Use modern software for Bayesian data analysis.
    • Understand the role of prior distributions.
    • Assess the fit of Bayesian models.

    Timetable

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught On Campus
    Learning management system
    Other

    Lecture

    Stream Days Times Weeks
    Attend
    A1 Tuesday 09:00-09:50 28-34, 36-41
    Wednesday 09:00-09:50 28-34, 36-41
    Thursday 09:00-09:50 28-34, 36-41

    Tutorial

    Stream Days Times Weeks
    Attend
    A1 Thursday 15:00-16:50 28-34, 36-41

    Overview

    Introduction to Bayesian methods with an emphasis on data analysis. Topics include prior choice, posterior assessment, hierarchical modelling and model fitting using R, JAGS and other freely available software.

    About this paper

    Paper title Bayesian Data Analysis
    Subject Statistics
    EFTS 0.15
    Points 18 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees Tuition Fees for 2024 have not yet been set
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Prerequisite
    STAT 260 and (STAT 261 or STAT 270)
    Restriction
    STAT 423
    Schedule C
    Arts and Music, Science
    Contact

    matthew.schofield@otago.ac.nz

    Teaching staff

    Associate Professor Matthew Schofield

    Textbooks

    To be determined.

    Course outline

    View the course outline for STAT371

    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

    On successful completion of the paper, students will be able to:

    • Understand the difference between Bayesian and frequentist statistics
    • Fit and interpret basic statistical models using Bayesian inference
    • Use modern software for Bayesian data analysis
    • Understand the role of prior distributions
    • Assess the fit of Bayesian models

    Timetable

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught On Campus
    Learning management system
    Other

    Lecture

    Stream Days Times Weeks
    Attend
    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

    Tutorial

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