STATS226-18B (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)

: rachael.foote@waikato.ac.nz

Placement 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 or 9 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.
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Paper Description

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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.


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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 and explain the theory behind Bayesian inference for discrete and continuous random variables.
    Linked to the following assessments:
  • Apply Bayesian inference in the case of the event probability for a binomial distribution.
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  • Apply Bayesian inference in the case of the event rate (i.e. mean) for a Poisson distribution.
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  • Apply Bayesian inference in the case of the mean for a normal distribution.
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  • Use Bayesian inference in the context of comparing two population means.
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  • Use Bayesian inference in the context of simple linear regression.
    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. 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 Wednesday 15th August 2018, from 9am (Week 6)

Test Two Wednesday 10th October 2018, from 9am (Week 12)

The tests are ‘restricted book’. You may take in one sheet of A4 paper with self-compiled, handwritten notes for each test. You may write on both sides of a sheet.

Exam: There will be a 3-hour 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 self-compiled, handwritten notes for the examination. You may write on both sides of each sheet.

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Assessment Components

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The internal assessment/exam ratio (as stated in the University Calendar) is 50:50. There is no final exam. The final exam makes up 50% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 50:50 or 0:0, whichever is more favourable for the student. The final exam makes up either 50% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Test 1
15 Aug 2018
9:00 AM
15
  • In Class: In Lecture
2. Test 2
10 Oct 2018
9:00 AM
15
  • In Class: In Lecture
3. Assignments 1 to 10
20
  • Hand-in: Assignment Box (G Block)
4. Exam
50
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).
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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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