STATS22619B (HAM)
Bayesian Statistics
15 Points
Staff
Convenor(s)
Lyn Hunt
8338
G.3.21
To be advised
lyn.hunt@waikato.ac.nz

Lecturer(s)
Paul Brown
5279
G.3.30
To be advised
paul.brown@waikato.ac.nz

Lyn Hunt
8338
G.3.21
To be advised
lyn.hunt@waikato.ac.nz

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Paper Description
This paper STATS226 introduces statistical methods from a Bayesian perspective which gives a coherent approach to the problem of revising beliefs given relevant data.
In this paper, we consider the concepts of logic, probability and uncertainty, and show how to use these for Bayesian inference on discrete and continuous random variables, including the Binomial proportion, Poisson mean, Normal mean, and how these can be adapted to consider Bayesian inference for the difference between two Normal means and in Simple Linear Regression.
The theory is presented, but we also make use of R functions written to perform the Bayesian analysis.
The Bayesian approach is compared to the Frequentist (or classical) inferential approach to highlight similarities and differences.
Paper Structure
Learning Outcomes
Students who successfully complete the course should be able to:
Assessment
The internal assessment for this course will consist of:
Two tests, each worth 30% of the internal component (i.e. 15% of your final mark each, overall)
Ten tutorial assignments, each worth 4% of the internal component (i.e. 2% of your final mark each, overall)
Tests: There will be two tests (held during lecture times):
Test One Monday 12 August 2019, 11am (Week 6)
Test Two Monday 7 October 2019, 11am (Week 12)
The tests are ‘restricted book’. You may take in one sheet of A4 paper with selfcompiled, handwritten notes for each test. You may write on both sides of a sheet.
Exam: There will be a 3hour final exam for this course. You must sit the final exam to complete the course. The exam contributes 50% of your overall mark.
The exam is 'restricted book'. You may take in two sheets of A4 paper with selfcompiled, handwritten notes for the examination. You may write on both sides of each sheet.
Assessment Components
The internal assessment/exam ratio (as stated in the University Calendar) is 50:50. The final exam makes up 50% of the overall mark.
Required and Recommended Readings
Recommended Readings
Other Resources
We will be making use of the R statistical software package in this course. R is available in the Rblock computer labs. R is opensource software which is freely available for personal use. You can download your own copy of R from cran.rproject.org, along with any accompanying Rpackages you desire.
In addition, you might also like to download the RStudio software. This provides a more userfriendly interface to the R program (you will also need to download R itself to use RStudio). RStudio is also opensource and freely available: www.rstudio.com
Online Support
Workload
Your minimum expected workload for this paper is a 1012 hours per week, including the scheduled times for lectures and tutorials.
Linkages to Other Papers
Prerequisite(s)
Prerequisite papers: At least one of MATH101, MATH102, MATHS101, MATHS102, STAT111, STAT121, STATS111, or STATS121.
Restriction(s)
Restricted papers: STAT226