STATS226-20B (HAM)

Bayesian Statistics

15 Points

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Division of Health Engineering Computing & Science
School of Computing and Mathematical Sciences
Department of Mathematics and Statistics

Staff

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Convenor(s)

Lecturer(s)

Administrator(s)

: maria.admiraal@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: debby.dada@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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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 COVID-19 alert levels will change and we have to move to a fully online-only 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.


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Paper Structure

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There are three contact hours per week. These will usually involve lectures, but one of these hours might be given as a tutorial, subject to requirement.
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Learning Outcomes

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Students who successfully complete the course should be able to:

  • Understand the basics of mathematical statistics and probability
    Linked to the following assessments:
  • Understand and explain theory behing Frequentist inference
    Linked to the following assessments:
  • Understand and explain the theory behind Bayesian inference
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  • Understand the similarities and differences between Bayesian and Frequentist approaches
    Linked to the following assessments:
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Assessment

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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 COVID-19 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 COVID-19 restrictions.

Exam: There will be NO FINAL EXAM! (because of COVID-19 changes)

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Test 1
21 Aug 2020
No set time
30
  • Other:
2. Test 2
16 Oct 2020
No set time
30
  • Other:
3. Assignments 1 to 4
40
  • Other:
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Recommended Readings

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Introduction to Bayesian Statistics - 2nd Edition, by William M. Bolstad
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Other Resources

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We will be making use of the R statistical software package in this course. R is available in the R-block computer labs. R is open-source software which is freely available for personal use. You can download your own copy of R from cran.r-project.org, along with any accompanying R-packages you desire.

In addition, you might also like to download the R-Studio software. This provides a more user-friendly interface to the R program (you will also need to download R itself to use R-Studio). R-Studio is also open-source and freely available: www.rstudio.com

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Online Support

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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.
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Workload

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Your minimum expected workload for this paper is a 10-12 hours per week, including the scheduled times for lectures and tutorials.

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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: At least one of MATH101, MATH102, MATHS101, MATHS102, STAT111, STAT121, STATS111, or STATS121.

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: STAT226

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