STATS22620B (HAM)
Bayesian Statistics
15 Points
Staff
Convenor(s)
Chaitanya Joshi
4019
G.3.22
To be advised
chaitanya.joshi@waikato.ac.nz

Lecturer(s)
Chaitanya Joshi
4019
G.3.22
To be advised
chaitanya.joshi@waikato.ac.nz

Han Gan
G.3.28
To be advised
han.gan@waikato.ac.nz

Administrator(s)
Librarian(s)
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Paper Description
STATS226 is the first 'theory' paper that gives insights into the conceptual and mathematical aspects of the basic statistical theory.
In this paper, we will consider the concepts of logic, probability and uncertainty, discrete and continuous distributions, likelihood, basics of Frequentist (or classical) and Bayesian inference.
The Bayesian approach is compared to the Frequentist inferential approach to highlight similarities and differences.
Note: It is possible that the COVID19 alert levels will change and we have to move to a fully onlineonly teaching. This may alter the teaching pace and effectiveness and therefore the topics that are able to be covered in the paper in such cirumstances.
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. 30% of your final mark each, overall)
Four assignments, each worth 10% of the internal component (i.e. 10% of your final mark each, overall)
Tests: There will be two online/take home tests (due to COVID19 changes)
Test One in Week 6
Test Two in Week 12
The details on the exact day/time and nature of the test will be advertised two weeks prior and will be subject to what is allowed under the existing COVID19 restrictions.
Exam: There will be NO FINAL EXAM! (because of COVID19 changes)
Assessment Components
The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam.
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
All information relating to this paper, including your internal assessment marks, will be posted to the STATS226 Moodle page (elearn.waikato.ac.nz).
All material and lecture recordings will be available online for remote access. Online Zoom help sessions will also be organised as needed.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