STATS 4023 - Computational Bayesian Statistics - Honours
North Terrace Campus - Semester 2 - 2023
-
General Course Information
Course Details
Course Code STATS 4023 Course Computational Bayesian Statistics - Honours Coordinating Unit 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 Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107 Restrictions Honours students only Assessment Ongoing assessment and examination. Course Staff
Course Coordinator: Dr John Maclean
Course Timetable
The full timetable of all activities for this course can be accessed from .
-
Learning Outcomes
Course Learning Outcomes
- To understand the principles of Bayesian inference and its mathematical basis.
- To understand the application of Bayesian inference in a variety of practical settings.
- To understand the computational methods used for Bayesian inference, with a focus on Markov Chain Monte Carlo methods.
- The ability to implement Markov Chain Monte Carlo Methods in R.
- The ability to apply Bayesian methods and computational techniques using 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) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1,2,3,4,5 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1,2,3,4,5 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
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.- 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 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:
- 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.
Assessment Summary
Item Number Weight Total Assignments 3 5%,5%,10% 20% Tests 3 10% 30% Practical exam 1 20% 20% Written 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:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN 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 .
-
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
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
-
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
-
Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
