STATS 4023 - Computational Bayesian Statistics III - Honours

North Terrace Campus - Semester 2 - 2021

The aim of this course is to equip students with the theoretical knowledge and practical skills to perform Bayesian inference in a wide range of practical applications. Following an introduction to the Bayesian framework, the course will focus on the main Markov chain Monte Carlo algorithms for performing inference and will consider a number of models widely used in practice. Topics covered are: Introduction to Bayesian statistics; model checking, comparison and choice; introduction to Bayesian computation; Gibbs sampler; Metropolis-Hastings algorithm; missing data techniques; hierarchical models; regression models; Gaussian process models.

  • General Course Information
    Course Details
    Course Code STATS 4023
    Course Computational Bayesian Statistics III - Honours
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites MATHS 2103 or MATHS 2107 or STATS 2107
    Assumed Knowledge Proficiency in at least one of R, Python, MATLAB or Julia
    Restrictions Honours students only
    Course Description The aim of this course is to equip students with the theoretical knowledge and
    practical skills to perform Bayesian inference in a wide range of practical applications. Following an introduction to the Bayesian framework, the course will focus on the main Markov chain Monte Carlo algorithms for performing inference and will consider a number of models widely used in practice. Topics covered are: Introduction to Bayesian statistics; model checking, comparison and choice; introduction to Bayesian computation; Gibbs sampler; Metropolis-Hastings algorithm; missing data techniques; hierarchical models; regression models; Gaussian process models.
    Course Staff

    Course Coordinator: Associate Professor Gary Glonek

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    1. To understand the principles of Bayesian inference and its mathematical basis.
    2. To understand the application of Bayesian inference in a variety of practical settings.
    3. To understand the computational methods used for Bayesian inference, with a focus on Markov Chain Monte Carlo methods.
    4. The ability to implement simple Markov Chain Monte Carlo Methods in R.
    5. The ability to apply Bayesian methods and computational techniques using packages such as Stan to solve data analytic problems.
    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)
    1,2,3,4,5
    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
    1,2,3,4,5
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1,2,3,4,5
  • Learning Resources
    Required Resources
    There is no presecribed text for this course. Lecture notes are provided.
    Recommended Resources
    The following resources are recommended.
    1. Bayesian Data Analysis, 3rd Edition. Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Chapman & Hall/CRC 2014.
    2. Handbook of Markov Chain Monte Carlo. Edited by Steve Brooks, Andrew Gelman, Galin L Jone, Xiaoli Meng. Chapman & Hall/CRC 2011.
    Online Learning
    This course uses MyUni-Canvas for providing course materials and resources, including lecture notes, assignment papers, tutorial and computing worksheets, solutions, project materials and so on. Students should check their email and MyUni announcements for this course regularly for any notices or correspondence from the Course Coordinator and tutors.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Content will be delivered in a series of weekly topic videos that students watch independently.
    There will be weekly workshops in one of the timetabled lecture slots in which the video content will be discussed.
    The content will be reviewed in a series of 4 online quizzes.
    Students will reinforce the practice and theory in a series of tutorial and practical sessions.
    Workload

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

    Activity Number Workload Hours
    Topic Videos 12 48
    Workshops 12 12
    Tutorials 6 18
    Practicals 6 18
    Quizzes 4 4
    Tests 2 2
    Assignments 4 54
    Total 156
    Learning Activities Summary
    Week 1: Introduction to Bayesian Inference, conjugate priors.
    Week 2: Uninformative priors, Jeffreys priors, improper priors, two-parameter normal problems.
    Week 3: Numerical integration, direct simulation and rejection sampling.
    Week 4: Hierarchical models, review of Markov Chains.
    Week 5: Markov Chain Monte Carlo, the Gibbs Sampler.
    Week 6: The Metropolis Hastings Algorithm.
    Week 7: Convergence of MCMC iterations.
    Week 8: Hamiltonian Monte Carlo.
    Week 9: Approximate Bayesian Computation.
    Week 10:  Computing with STAN.
    Week 11: Bayesian regression models.
    Week 12: Gaussian process models.
  • 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
    Item Number Weighting Total
    Assignments 4 5% 20%
    Quizzes 4 5% 20%
    Tests 2 15% 30%
    Exam 1 30% 30%
    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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 (http://www.adelaide.edu.au/policies/101/) 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
  • Fraud Awareness

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