STATS 3023 - Computational Bayesian Statistics III
North Terrace Campus - Semester 2 - 2020
General Course Information
Course Code STATS 3023 Course Computational Bayesian Statistics III 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. 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 Coordinator: Associate Professor Gary Glonek
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesThere is no presecribed text for this course. Lecture notes are provided.
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
There is no presecribed text for this course. Lecture notes are provided.
Recommended ResourcesThe following resources are recommended.
- Bayesian Data Analysis, 3rd Edition. Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Chapman & Hall/CRC 2014.
- Handbook of Markov Chain Monte Carlo. Edited by Steve Brooks, Andrew Gelman, Galin L Jone, Xiaoli Meng. Chapman & Hall/CRC 2011
Online LearningThis 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 ModesContent 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.
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 SummaryWeek 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.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Item Number Weight Total Assignments 4 5% 20% Quizzes 4 5% 20% Tests 2 15% 30% Final exam 1 30% 30%
No information currently available.
No information currently available.
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.
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.
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