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STAT371 Bayesian Data Analysis

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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.

Paper title Bayesian Data Analysis
Paper code STAT371
Subject Statistics
EFTS 0.15
Points 18 points
Teaching period Semester 2 (On campus)
Domestic Tuition Fees (NZD) $929.55
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

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STAT 260 and (STAT 261 or STAT 270)
Schedule C
Arts and Music, Science

Dr Peter Dillingham

Teaching staff

Dr. Peter Dillingham

Dr. Matthew Schofield


To be determined

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.

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Semester 2

Teaching method
This paper is taught On Campus
Learning management system


Stream Days Times Weeks
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


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