STATS 7004 - Statistics Topic A

North Terrace Campus - Semester 1 - 2016

Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at

  • General Course Information
    Course Details
    Course Code STATS 7004
    Course Statistics Topic A
    Coordinating Unit Mathematical Sciences
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Available for Study Abroad and Exchange Y
    Assessment Ongoing assessment 30%, exam 70%
    Course Staff

    Course Coordinator: Associate Professor Robb Muirhead

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    In 2016, the topic of this course is Statistical Decision Theory and Bayesian Statistics.


    This course introduces students to the basic elements of statistical decision theory and Bayesian methodology, and the connections between the two. It will include the following topics: Structure of a decision problem, decision rules, expected loss, risk, fundamentals of Bayesian analysis, admissibility of decision rules, minimax analysis, , complete classes of decision rules, Bayesian inference, types of prior distributions, conjugate analysis, predictive distributions and hierarchical Bayesian models.

    Pre-requisites: Mathematical Statistics III (STATS 3006) and Statistical Modelling III (STATS 3001), or equivalent knowledge.

    Learning Outcomes:

    On successful completion of this course, students should be able to:

    1. demonstrate their understanding of advanced principles of statistical decision theory and Bayesian inference;
    2. understand the basic elements of a formal decison problem;
    3. recognise how to establish optimality properties of decision rules
    4. demontrate understanding of the Bayesian approach to statistical inference and apply Bayesian methods in practice.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
  • Learning Resources
    Required Resources
    Recommended Resources
    There are many books that deal with either Statistical Decision Theory or Bayesian Statistics, or both -- and new ones are published every year. The ones that are most relevant to this particular course will be listed in a handout given out on the first day of classes.
    Online Learning
    The lecture notes, assignments and other materials will be made available on the lecturer's webpage. Students should check their email regularly for any course notices or correspondence.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course will be delivered through 30 presented lectures over the semester. There will be four written assignments and a test in Week 7 to provide students with the opportunity to gauge their progress.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity Workload Hours
    Lectures 30 90
    Assignments 4 40
    Test 1 26
    Total 156
    Learning Activities Summary
    Lecture Outline

    1. Basics of Statistical Decision Theory (lectures 1-8)
    2. Optimality properties of decision rules (admissibility, minimaxity)(lectures 9-15)
    3. Bayesian inference and the Baryesian paradigm (lectures 16-20)
    4. Predictive distributions (lectures 21-25)
    5. Hierarchical models (lectures 26-30)
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Activity Weighting Objective 
    Assignments 20% all
    Test 10% all
    Exam 70% all
    Assessment Related Requirements
    A final aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    Assessment Task Distributed Due Weighting
    Assignment 1 Week 1 Week 3 5%
    Assignment 2 Week 3 Week 6 5%
    Assignment 3 Week 6 Week 9 5%
    Assignment 4 Week 9 Week 12 5%
    Test Week 7 10%
    Final exam Examination period 70%
    1. All written assignments are to be submitted to the designated hand-in boxes within the School of Mathematical Sciences, or to the lecturer, with a signed cover sheet attached.
    2. Late assignments will not be accepted unless with by prior agreement with the lecturer. Please discuss delays owing to medical or compassionate reasons with the lecturer.
    3. Marked assignments will usually be returned to students within two weeks of submission.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
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